The proliferation of hate speech on social media presents an escalating threat to both public discourse and individual mental well-being. Traditional strategies that prioritize detection and removal often neglect to engage directly with hate speakers or address the underlying causes of their hostility. This article proposesHateMediator, a dialogue-based intervention framework that fine-tunes large language models (LLMs) to generate persuasive, context-aware counter-hate speech. The framework emphasizes two core aspects: the generation of effective counter-hate responses and their evaluation through multiturn dialogues. Our fine-tuning approach integrates tutorial-based learning with critical token guidance, enabling LLMs to recognize and reproduce strategic rhetorical patterns observed in expert interventions. To support training and evaluation, we introduce theMedHatedataset, grounded in social science theory, comprising complete dialogue records from 85 real-world hate incidents (including 255 dialogues), expert-crafted counter-responses, and feedback from the original hate speakers. Experimental results show thatHateMediatorconsistently outperforms baseline LLMs across multiple evaluation dimensions. This study advances both the technical frontier of hate speech intervention and the ethical deployment of LLMs in addressing complex social issues.

Transformer-based models have demonstrated great promises in single image super-resolution (SISR), but our investigations find significant redundancy in terms of high mutual information across the attention maps, which is associated with reduced efficiency and degraded performance of SOTA models. To address the problem, here we propose a low redundancy attention network (LRAN). First, to mitigate the redundancy among heads, we introduce in the self-attention computation a multi-element mechanism, which allows for the incorporation of various types of self-attention, thus increasing inter-head diversity. Second, to address the redundancy among blocks, we propose the encapsulated architecture, in which enhanced local perception unit and gated multi-layer perceptron are designed to capture local information. Specifically, this architecture incorporates a single self-attention layer between several MLP layers. Subsequently, the proposed gated multi-layer perceptron significantly enhances the SR quality. Extensive experiments demonstrate that LRAN outperforms SOTA models in the task of lightweight SR, achieving a better trade-off between quality and speed. For instance, the proposed LRAN-light surpasses SwinIR-light by 0.32dB PSNR in $\times 4$ SR on Urban100, while running $\times 4$ faster.

Change captioning is a task that describes changes in image pairs using natural language. This task is more complex than single-image captioning as it requires a comprehensive understanding of each image and the ability to recognize and describe the semantic changes in image pairs. The key challenge lies in making the network generate an accurate and stable change representation under the interference of viewpoint shift. In this paper, we propose a cross-view and multi-step interaction network to generate robust change representation to resist pseudo-change. Specifically, in the intra-image representation learning stage, a cross-view interaction encoder is designed to enhance internal relationships by cross-referencing in image pairs. In the change feature learning stage, a multi-step change perceptron is employed to capture the change semantics from coarse to fine progressively. Then, a fusion module dynamically combines them as a fine-grained change representation. Besides, we propose a backward representation reconstruction module that facilitates the capture of semantic changes, thus improving the quality of captions in a self-supervised manner. Extensive experiments have shown that the method effectively captures real semantic changes under the interference of viewpoint shift and achieves state-of-the-art performance on five public datasets. The code is available at https://github.com/TTXiann/CVMSI

Gaussian processes have been used to model the vector field of continuous dynamical systems, which are characterized by a probabilistic ordinary differential equation (GP-ODE). Bayesian inference for these models has been extensively studied and applied in tasks such as time series prediction. However, the use of standard GPs with basic kernels like squared exponential kernels has been common in GP-ODE research, limiting the model’s ability to represent complex scenarios. To address this limitation, we introduce normalizing flows to reparameterize the ODE vector field, resulting in a data-driven prior distribution, thereby increasing flexibility and expressive power. We develop a variational inference algorithm that utilizes analytically tractable probability density functions of normalizing flows. Additionally, we also apply normalizing flows to the posterior inference of GP-ODEs to resolve the issue of strong mean-field assumptions. By applying normalizing flows in these ways, our model improves accuracy and uncertainty estimates for Bayesian GP-ODEs. We validate the effectiveness of our approach on simulated dynamical systems and real-world human motion data, including time series prediction and missing data recovery tasks.

Density ratio estimation is fundamental to tasks involving f-divergences, yet existing methods often fail under significantly different distributions or inadequately overlapping supports --- the density-chasm and the support-chasm problems. Additionally, prior approaches yield divergent time scores near boundaries, leading to instability. We design $\textbf{D}^3\textbf{RE}$, a unified framework for robust, stable and efficient density ratio estimation. We propose the dequantified diffusion bridge interpolant (DDBI), which expands support coverage and stabilizes time scores via diffusion bridges and Gaussian dequantization. Building on DDBI, the proposed dequantified Schr{\"o}dinger bridge interpolant (DSBI) incorporates optimal transport to solve the Schr{\"o}dinger bridge problem, enhancing accuracy and efficiency. Our method offers uniform approximation and bounded time scores in theory, and outperforms baselines empirically in mutual information and density estimation tasks.

In the field of blind image quality assessment, accurately capturing localized distortions and structural inconsistencies within images remains a significant challenge. To tackle this issue, we propose BlockIQA, a novel framework that enhances local sensitivity through deep block analysis. BlockIQA divides images into non-overlapping blocks and employs a multi-branch architecture that integrates ResNet50, the Feature Pyramid Network, and an Auxiliary Feature Extraction Layer. The primary innovations of this paper include: (1) Segmenting images into smaller blocks for detailed analysis and employing a Gaussian similarity model that dynamically adapts to variations in feature dimensions and directional consistency. This approach enables more precise characterization of local image features. (2) Achieving a balance between global semantic information and localized distortion patterns through multiscale feature fusion using feature pyramid networks and auxiliary feature extraction layer. This ensures that while capturing overall image semantics, no fine local distortion information is overlooked. Experimental results demonstrate that BlockIQA performs well across datasets with various types of distortions and exhibits strong generalizability across different databases. In summary, BlockIQA pioneers a new deep learning architectural paradigm for blind image quality assessment. Its design philosophy of enhancing local sensitivity through deep block analysis provides valuable new ideas and methods for research and practice in this domain.

Diffusion models (DMs) excel in image generation but suffer from slow inference and training-inference discrepancies. Although gradient-based solvers for DMs accelerate denoising inference, they often lack theoretical foundations in information transmission efficiency. In this work, we introduce an information-theoretic perspective on the inference processes of DMs, revealing that successful denoising fundamentally reduces conditional entropy in reverse transitions. This principle leads to our key insights into the inference processes: (1) data prediction parameterization outperforms its noise counterpart, and (2) optimizing conditional variance offers *a reference-free way* to minimize both transition and reconstruction errors. Based on these insights, we propose an entropy-aware variance optimized method for the generative process of DMs, called *EVODiff*, which systematically reduces uncertainty by optimizing conditional entropy during denoising. Extensive experiments on DMs validate our insights and demonstrate that our method significantly and consistently outperforms state-of-the-art (SOTA) gradient-based solvers. For example, compared to the DPM-Solver++, EVODiff reduces the reconstruction error by up to *45.5\%* (FID improves from 5.10 to 2.78) at 10 function evaluations (NFE) on CIFAR-10, cuts the NFE cost by *25\%* (from 20 to 15 NFE) for high-quality samples on ImageNet-256, and improves text-to-image generation while reducing artifacts. Code is available at https://github.com/ShiguiLi/EVODiff.

Time series forecasting is crucial in areas like smart grids, traffic flow management, and financial analysis, especially for Long-sequence Time Series Forecasting (LTSF) tasks. We propose WaveSparseTSF, a lightweight LTSF model designed to handle complex temporal dependencies while minimizing computational costs. It integrates wavelet transform and cross-period sparse forecasting, which separate the data into low and high frequency components and focus on periodic trends to reduce complexity. By processing raw sequences through wavelet decomposition and downsampling, WaveSparseTSF effectively captures both global trends and local fluctuations. Despite using only around 1k parameters, it achieves competitive or superior results compared to state-of-the-art models and demonstrates strong generalization capabilities. As a result, WaveSparseTSF is particularly well-suited for environments with limited resources, small datasets, or low-quality data.

Hand-eye calibration is a crucial step in the implementation of vision-based robotic arm systems. However, existing calibration methods struggle to adapt to scenarios where the robotic arm or vision system frequently changes position. To address this challenge, this paper proposes a novel calibration method based on an exploration-optimized Twin Delayed DDPG (TD3) algorithm enhanced with kernel density estimation and decaying noise, referred to as E-TD5. The proposed approach not only incorporates the E-TD5 algorithm to improve the exploration and exploitation capabilities of the TD3 reinforcement learning framework but also introduces an adaptive target-perception enhancement system to handle frequent variations in hand-eye positioning. Experimental results validate the effectiveness of the proposed method, its robustness to changes in hand-eye positions, and the significant advantages of the E-TD5 algorithm compared to the standard TD3 approach.

Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their flexibility and non-linear nature. An importance-weighted version of the Bayesian GPLVMs has been proposed to obtain a tighter variational bound. However, this version of the approach is primarily limited to analyzing simple data structures, as the generation of an effective proposal distribution can become quite challenging in high-dimensional spaces or with complex data sets. In this work, we propose VAIS-GPLVM, a variational Annealed Importance Sampling method that leverages time-inhomogeneous unadjusted Langevin dynamics to construct the variational posterior. By transforming the posterior into a sequence of intermediate distributions using annealing, we combine the strengths of Sequential Monte Carlo samplers and VI to explore a wider range of posterior distributions and gradually approach the target distribution. We further propose an efficient algorithm by reparameterizing all variables in the evidence lower bound (ELBO). Experimental results on both toy and image datasets demonstrate that our method outperforms state-of-the-art methods in terms of tighter variational bounds, higher log-likelihoods, and more robust convergence.

Remote Sensing Image Change Captioning (RSICC) plays a critical role in automated environmental monitoring by generating natural language descriptions that provide intuitive interpretations of changes between bi-temporal remote sensing images. Despite recent advancements, existing methods suffer from two fundamental limitations: (1) static encoding architectures fail to account for scene complexity and semantic diversity during feature extraction, leading to inflexible representation learning; and (2) symmetric computational structures are inherently unsuitable for modeling the asymmetric temporal dependencies inherent in “before-to-after” image pairs. To address these challenges, we propose a Dynamic Asymmetric Encoder (DAE), which introduces two key innovations. First, we design a difference-guided dynamic convolution module that adaptively adjusts convolutional parameters using input-driven scaling factors and offsets, thereby enabling scene-aware intra-image feature enhancement. Second, we develop a Multi-expert Temporal Interaction (METI) module that establishes an asymmetric computational topology: the “after” image branch actively perceives change information relative to the “before” image through three heterogeneous interaction experts, followed by feature fusion. This design allocates greater computational capacity to the “after” image branch while preserving temporal coherence. Furthermore, we introduce a Multi-level Feature Aggregator (MFA) that enhances the representation of salient changed regions across multiple scales via an iterative reinforcement mechanism. Experimental results validate the effectiveness of the proposed method, demonstrating state-of-the-art performance on two benchmark RSICC datasets. We publicly release our code repository at https://github.com/TTXiann/Dynamic-Asymmetric to facilitate future research.

Deep Gaussian process (DGP) models offer a powerful nonparametric approach for Bayesian inference, but exact inference is typically intractable, motivating the use of various approximations. However, existing approaches, such as mean-field Gaussian assumptions, limit the expressiveness and efficacy of DGP models, while stochastic approximation can be computationally expensive. To tackle these challenges, we introduce neural operator variational inference (NOVI) for DGPs. NOVI uses a neural generator to obtain a sampler and minimizes the regularized Stein discrepancy (RSD) between the generated distribution and true posterior in $\mathcal {L}_{2}$ space. We solve the minimax problem using Monte Carlo estimation and subsampling stochastic optimization techniques and demonstrate that the bias introduced by our method can be controlled by multiplying the Fisher divergence with a constant, which leads to robust error control and ensures the stability and precision of the algorithm. Our experiments on datasets ranging from hundreds to millions demonstrate the effectiveness and the faster convergence rate of the proposed method. We achieve a classification accuracy of 93.56 on the CIFAR10 dataset, outperforming state-of-the-art (SOTA) Gaussian process (GP) methods. We are optimistic that NOVI possesses the potential to enhance the performance of deep Bayesian nonparametric models and could have significant implications for various practical applications.

The theory of Bayesian learning incorporates the use of Student-t Processes to model heavy-tailed distributions and datasets with outliers. However, despite Student-t Processes having a similar computational complexity as Gaussian Processes, there has been limited emphasis on the sparse representation of this model. This is mainly due to the increased difficulty in modeling and computation compared to previous sparse Gaussian Processes. Our motivation is to address the need for a sparse representation framework that reduces computational complexity, allowing Student-t Processes to be more flexible for real-world datasets. To achieve this, we leverage the conditional distribution of Student-t Processes to introduce sparse inducing points. Bayesian methods and variational inference are then utilized to derive a well-defined lower bound, facilitating more efficient optimization of our model through stochastic gradient descent. We propose two methods for computing the variational lower bound, one utilizing Monte Carlo sampling and the other employing Jensen's inequality to compute the KL regularization term in the loss function. We propose adopting these approaches as viable alternatives to Gaussian processes when the data might contain outliers or exhibit heavy-tailed behavior, and we provide specific recommendations for their applicability. We evaluate the two proposed approaches on various synthetic and real-world datasets from UCI and Kaggle, demonstrating their effectiveness compared to baseline methods in terms of computational complexity and accuracy, as well as their robustness to outliers.

Transformer-based models have demonstrated impressive performance in image super-resolution (SR), but they come with a high computational overhead. In this paper, we present a low redundant attention network (LRAN) for efficient image SR. We observe that there is significant similarity in attention maps across heads and blocks, leading to computational redundancy. First, to mitigate the redundancy in attention maps among heads, we introduce a multi-element mechanism in the self-attention computation. This mechanism allows for the incorporation of various types of self-attention, thus increasing inter-head diversity. Second, to address this redundancy in attention maps among blocks, we propose the hamburger architecture, which introduces enhanced local perception units to capture local information. Moreover, this architecture incorporates a single self-attention layer between several efficient MLP layers. Extensive experiments demonstrate that LRAN outperforms the latest models in lightweight SR, achieving a better trade-off between SR quality and latency. For instance, LRAN surpasses SwinIR-light by 0.25dB PSNR in ×4 SR on Urban100, while running ×5 faster.

Deep Gaussian processes (DGPs) provide a robust paradigm in Bayesian deep learning. In DGPs, a set of sparse integration locations called inducing points are selected to approximate the posterior distribution of the model. This is done to reduce computational complexity and improve model efficiency. However, inferring the posterior distribution of inducing points is not straightforward. Traditional variational inference techniques methods to approximate the posterior often leads to significant bias. To address this issue, we propose an alternative named Denoising Diffusion Variational Inference (DDVI) that utilizes a denoising diffusion stochastic differential equation (SDE) for generating posterior samples of inducing variables. We refer to the score matching method in the denoising diffusion model to approximate challenging score functions using a neural network. Furthermore, by combining classical mathematical theory of SDE with the minimization of KL divergence between the approximate and true processes, we propose a novel explicit variational lower bound for the marginal likelihood function of DGP. Through extensive experiments on various datasets and comparisons with baseline methods, we empirically demonstrate the effectiveness of the DDVI method in posterior inference of inducing points for DGP models.

The Internet-of-Things (IoT) technology is becoming increasingly pivotal in the financial services sector, with a growing number of algorithms being employed in high-frequency trading. High-frequency prediction in financial time series prediction presents a promising avenue of research. From convolutional neural networks to recurrent neural networks, deep learning have demonstrated exceptional capabilities in capturing the nonlinear characteristics of stock markets, thereby achieving high performance in stock index prediction. In this paper, we employ ODE-LSTM model for high-frequency price forecasting, predicting stock price data across various time scales, including 1-minute, 5-minutes, and 30-minutes frequencies. This approach introduces a novel concept, wherein the LSTM (Long Short-Term Memory) model is integrated with Neural ODE (Ordinary Differential Equations) to manage the hidden state and augment model interpretability. Over the course of 7 months, we achieved a 41.79% excess return on a simulated trading platform, with a daily average excess return of 0.30%, showcasing the commendable performance of our model and strategy.

We devise a new regularization for denoising with self-supervised learning. The regularization uses a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we again denoise after ``re-noising.'' The network is updated to minimize the discrepancy between the twice-denoised image and its prior. We demonstrate that this regularization enables the network to learn to denoise even if it has not seen any clean images. The effectiveness of our method is based on the fact that CNNs naturally tend to capture low-level image statistics. Since our method utilizes the image prior implicitly captured by the deep denoising CNN to guide denoising, we refer to this training strategy as an Implicit Deep Denoiser Prior (IDDP). IDDP can be seen as a mixture of learning-based methods and traditional model-based denoising methods, in which regularization is adaptively formulated using the output of the network. We apply IDDP to various denoising tasks using only observed corrupted data and show that it achieves better denoising results than other self-supervised denoising methods.

Multi-scale architectures and attention modules have shown effectiveness in many deep learning-based image de-raining methods. However, manually designing and integrating these two components into a neural network requires a bulk of labor and extensive expertise. In this article, a high-performance multi-scale attentive neural architecture search (MANAS) framework is technically developed for image de-raining. The proposed method formulates a new multi-scale attention search space with multiple flexible modules that are favorite to the image de-raining task. Under the search space, multi-scale attentive cells are built, which are further used to construct a powerful image de-raining network. The internal multi-scale attentive architecture of the de-raining network is searched automatically through a gradient-based search algorithm, which avoids the daunting procedure of the manual design to some extent. Moreover, in order to obtain a robust image de-raining model, a practical and effective multi-to- one training strategy is also presented to allow the de-raining network to get sufficient background information from multiple rainy images with the same background scene, and meanwhile, multiple loss functions including external loss, internal loss, architecture regularization loss, and model complexity loss are jointly optimized to achieve robust de-raining performance and controllable model complexity. Extensive experimental results on both synthetic and realistic rainy images, as well as the down-stream vision applications (i.e., objection detection and segmentation) consistently demonstrate the superiority of our proposed method. The code is publicly available athttps://github.com/lcai-gz/MANAS.

Faithful measurement of perceptual quality is of significant importance to various multimedia applications. By fully utilizing reference images, full-reference image quality assessment (FR-IQA) methods usually achieve better prediction performance. On the other hand, no-reference image quality assessment (NR-IQA), also known as blind image quality assessment (BIQA), which does not consider the reference image, makes it a challenging but important task. Previous NR-IQA methods have focused on spatial measures at the expense of information in the available frequency bands. In this paper, we present a multiscale deep blind image quality assessment method (BIQA, M.D.) with spatial optimal-scale filtering analysis. Motivated by the multi-channel behavior of the human visual system and contrast sensitivity function, we decompose an image into a number of spatial frequency bands through multiscale filtering and extract features to map an image to its subjective quality score by applying convolutional neural network. Experimental results show that BIQA, M.D. compares well with existing NR-IQA methods and generalizes well across datasets.

Various image enhancement algorithms are adopted to improve underwater images that often suffer from visual distortions. It is critical to assess the output quality of underwater images undergoing enhancement algorithms, and use the results to optimise underwater imaging systems. In our previous study, we created a benchmark for quality assessment of underwater image enhancement via subjective experiments. Building on the benchmark, this paper proposes a new objective metric that can automatically assess the output quality of image enhancement, namely UWEQM. By characterising specific underwater physics and relevant properties of the human visual system, image quality attributes are computed and combined to yield an overall metric. Experimental results show that the proposed UWEQM metric yields good performance in predicting image quality as perceived by human subjects.

Continuous normalizing flows (CNFs) construct invertible mappings between an arbitrary complex distribution and an isotropic Gaussian distribution using Neural Ordinary Differential Equations (neural ODEs). It has not been tractable on large datasets due to the incremental complexity of the neural ODE training. Optimal Transport theory has been applied to regularize the dynamics of the ODE to speed up training in recent works. In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training. In this appoach, we optimize the network weights of the CNF alternately with evolutionary time by coordinate descent. Further with temporal regularization, stability of the evolution is ensured. This approach can be used in conjunction with the original regularization approach. We have experimentally demonstrated that the proposed approach can significantly accelerate training without sacrifying performance over baseline models.

Underwater image enhancement plays a critical role in marine industry. Various algorithms are applied to enhance underwater images, but their performance in terms of perceptual quality has been little studied. In this paper, we investigate five popular enhancement algorithms and their output image quality. To this end, we have created a benchmark, including images enhanced by different algorithms and ground truth image quality obtained by human perception experiments. We statistically analyse the impact of various enhancement algorithms on the perceived quality of underwater images. Also, the visual quality provided by these algorithms is evaluated objectively, aiming to inform the development of objective metrics for automatic assessment of the quality for underwater image enhancement. The image quality benchmark and its objective metric are made publicly available.

Saliency plays an important role in the area of image quality assessment. Image distortions cause shift/redistribution of saliency from its original places. There is a need to be able to measure such distortion-included saliency variation (DSV), so that the use of saliency can be optimised for automated image quality assessment. Effort has been made in our previous study to build a benchmark for the measurement of DSV through subjective testing. In this paper, we demonstrate that exiting similarity measures are unhelpful for the quantification of DSV. Thus, we propose a new metric for DSV combining local and global measures using convex optimization. The experimental results show that our proposed metric can accurately quantify saliency variation.

Time series clustering is usually an essential unsupervised task in cases when category information is not available and has a wide range of applications. However, existing time series clustering methods usually either ignore temporal dynamics of time series or isolate the feature extraction from clustering tasks without considering the interaction between them. In this article, a time series clustering framework named self-supervised time series clustering network (STCN) is proposed to optimize the feature extraction and clustering simultaneously. In the feature extraction module, a recurrent neural network (RNN) conducts a one-step time series prediction that acts as the reconstruction of the input data, capturing the temporal dynamics and maintaining the local structures of the time series. The parameters of the output layer of the RNN are regarded as model-based dynamic features and then fed into a self-supervised clustering module to obtain the predicted labels. To bridge the gap between these two modules, we employ spectral analysis to constrain the similar features to have the same pseudoclass labels and align the predicted labels with pseudolabels as well. STCN is trained by iteratively updating the model parameters and the pseudoclass labels. Experiments conducted on extensive time series data sets show that STCN has state-of-the-art performance, and the visualization analysis also demonstrates the effectiveness of the proposed model.

Convolution neural networks play an important role in the image classification tasks. However, it is time consuming to train the network and the cost of memory resources is usually high. In this paper, a simple and effective network named BinDCTNet is presented by using the binary discrete cosine transform(BinDCT) to extract the feature-maps and a hyper-parameter to reduce dimension of the extracted feature. The proposed network has extremely low computing complexity and there is almost no parameters needed to be stored. Experiments are carried out on the hand written digit dataset MNIST and the vehicle logo VLOGO dataset. The results show that the proposed network achieves the state-of-the-art accuracy with fast speed and low memory cost, which makes it applicable on mobile and embedded devices.

Recent CNN-based saliency models have achieved excellent performance on public datasets, but most are sensitive to distortions from noise or compression. In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to overcome this drawback. We construct a topological metric space where the implicit metric is determined by a deep network. In this latent space, we can group pixels within an observed image semantically into two regions, based on whether they are in a salient region or a non-salient region in the image. We carry out all feature extractions at the pixel level, which makes the output boundaries of the salient object finely-grained. Experimental results show that the proposed metric can generate robust salient maps that allow for object segmentation. By testing the method on several public benchmarks, we show that the performance of MEnet achieves excellent results. We also demonstrate that the proposed method outperforms previous CNN-based methods on distorted images.

We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and focuses the model on the structure of rain in images. This demonstrates that a deep architecture not only has benefits for high-level vision tasks but also can be used to solve low-level imaging problems. Though we train the network on synthetic data, we find that the learned network generalizes well to real-world test images. Experiments show that the proposed method significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. We discuss applications of this structure to denoising and JPEG artifact reduction at the end of the paper.

Epithelium-stroma classification is always considered as an important preprocessing step for morphological quantitative analysis in image-based histological researches of oncologic diseases. However, large-scale accurate ground-truth labeling is expensive in histopathological image analysis, thus the classification performances will still be limited with the insufficient labeled training samples. Considering that acquisition of public unlabeled histopathological images is much cheaper, an epithelium-stroma classification framework is developed, based on the deep convolutional neural network framework and the strategies of self-taught learning. The method has the ability of taking advantage of large-scale unlabeled public histopathological data as auxiliary data, and then transferring the knowledge to enhance the performances in epithelium-stroma classification with limited labeled training data. The experiments demonstrate that the proposed method outperforms traditional CNNs when the labeled training data size is decreasing dramatically.

We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighted variational model is proposed for better prior representation, which is imposed in the regularization terms. Different from conventional variational models, the proposed model can preserve the estimated reflectance with more details. Moreover, the proposed model can suppress noise to some extent. An alternating minimization scheme is adopted to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with other variational methods, the proposed method yields comparable or better results on both subjective and objective assessments.

In this letter, an effective image saliency detection method is proposed by constructing some novel spaces to model the background and redefine the distance of the salient patches away from the background. Concretely, given the backgroundness prior, eigendecomposition is utilized to create four spaces of background-based distribution (SBD) to model the background, in which a more appropriate metric (Mahalanobis distance) is quoted to delicately measure the saliency of every image patch away from the background. After that, a coarse saliency map is obtained by integrating the four adjusted Mahalanobis distance maps, each of which is formed by the distances between all the patches and background in the corresponding SBD. To be more discriminative, the coarse saliency map is further enhanced into the posterior probability map within Bayesian perspective. Finally, the final saliency map is generated by properly refining the posterior probability map with geodesic distance. Experimental results on two usual datasets show that the proposed method is effective compared with the state-of-the-art algorithms.

There is a growing interest in quantifying tissue susceptibility in MRI. However, the zeros in the dipole kernel makes the calculation of the magnetic susceptibility from the measured field to be an ill-posed problem. Recently, Bayesian regularization approaches have been utilized to enable accurate quantitative susceptibility mapping(QSM), such as L2 norm gradient minimization and TV. In this work, we propose an efficient QSM method by using a sparsity promoting regularization which called L0 norm of gradient to reconstruct susceptibility map. The use of L0 norm allows us to yield high quality image and prevent penalizing salient edges. Since the L0 minimization is an NP-hard problem, a special alternating optimization strategy by introducing an auxiliary variable is adopted to solve the problem and it only takes 1-2 mins to reconstruct the whole 3D susceptibility data. Both numerical phantom simulations and human brain tests are performed to demonstrate the superior performance of the proposed method compared with previous methods.

Pan-sharpening is the task of fusing spectral information in low resolution multispectral images with spatial information in a corresponding high resolution panchromatic image. In such approaches, there is a trade-off between spectral and spatial quality, as well as computational efficiency. We present a method for pan-sharpening in which a sparsity-promoting objective function preserves both spatial and spectral content, and is efficient to optimize. Our objective incorporates the l1/2-norm in a way that can leverage recent computationally efficient methods, and l1for which the alternating direction method of multipliers can be used. Additionally, our objective penalizes image gradients to enforce high resolution fidelity, and exploits the Fourier domain forfurther computational efficiency. Visual quality metrics demonstrate that our proposed objective function can achieve higher spatial and spectral resolution than several previous well-known methods with competitive computational efficiency.

In this letter, an effective enhancement method for remote sensing images is introduced to improve the global contrast and the local details. The proposed method constitutes an empirical approach by using the regularized-histogram equalization (HE) and the discrete cosine transform (DCT) to improve the image quality. First, a new global contrast enhancement method by regularizing the input histogram is introduced. More specifically, this technique uses the sigmoid function and the histogram to generate a distribution function for the input image. The distribution function is then used to produce a new image with improved global contrast by adopting the standard lookup table-based HE technique. Second, the DCT coefficients of the previous contrast improved image are automatically adjusted to further enhance the local details of the image. Compared with conventional methods, the proposed method can generate enhanced remote sensing images with higher contrast and richer details without introducing saturation artifacts.

In this paper, a new probabilistic method for image enhancement is presented based on a simultaneous estimation of illumination and reflectance in the linear domain. We show that the linear domain model can better represent prior information for better estimation of reflectance and illumination than the logarithmic domain. A maximum a posteriori (MAP) formulation is employed with priors of both illumination and reflectance. To estimate illumination and reflectance effectively, an alternating direction method of multipliers is adopted to solve the MAP problem. The experimental results show the satisfactory performance of the proposed method to obtain reflectance and illumination with visually pleasing enhanced results and a promising convergence rate. Compared with other testing methods, the proposed method yields comparable or better results on both subjective and objective assessments.

In this paper, a novel image enhancing approach focuses on single sandstorm image is proposed. The degraded image has some problems, such as color distortion, low-visibility, fuzz and non-uniform luminance, due to the light is absorbed and scattered by particles in sandstorm. The proposed approach based on fusion principles aims to overcome the aforementioned limitations. First, the degraded image is color corrected by adopting a statistical strategy. Then two inputs, which represent different brightness, are derived only from the color corrected image by applying Gamma correction. Three weighted maps (sharpness, chromaticity and prominence), which contain important features to increase the quality of the degraded image, are computed from the derived inputs. Finally, the enhanced image is obtained by fusing the inputs with the weight maps. The proposed method is the first to adopt a fusion-based method for enhancing single sandstorm image. Experimental results show that enhanced results can be improved by color correction, well enhanced details and local contrast while promoted global brightness, increasing the visibility, naturalness preservation. Moreover, the proposed algorithm is mostly calculated by per-pixel operation, which is appropriate for real-time applications.

This paper proposes a novel non-parametric Bayesian framework for solving mixed noise removal problem. In order to removing unstable effects of outlier noise such as salt-and-pepper in the training data, we decompose the observed data model into three components terms of ideal data, Gaussian noise and sparse outlier. And the proposed model employs spike-slab sparse prior to find the sparser coefficients of desired data term and outlier noise. Note that the proposed non-parametric Bayesian model can infer the noise statistics from the training data and have been robust to the mixed noise without tuning of model parameters. Experimental results demonstrate our proposed algorithm performs well with mixed noise and achieves better performance over other state-of-the-art methods.

Active contour models (ACMs) integrated with various kinds of external force fields to pull the contours to the exact boundaries have shown their powerful abilities in object segmentation. However, local minimum problems still exist within these models, particularly the vector field's "equilibrium issues." Different from traditional ACMs, within this paper, the task of object segmentation is achieved in a novel manner by the Poincaré map method in a defined vector field in view of dynamical systems. An interpolated swirling and attracting flow (ISAF) vector field is first generated for the observed image. Then, the states on the limit cycles of the ISAF are located by the convergence of Newton-Raphson sequences on the given Poincaré sections. Meanwhile, the periods of limit cycles are determined. Consequently, the objects' boundaries are represented by integral equations with the corresponding converged states and periods. Experiments and comparisons with some traditional external force field methods are done to exhibit the superiority of the proposed method in cases of complex concave boundary segmentation, multiple-object segmentation, and initialization flexibility. In addition, it is more computationally efficient than traditional ACMs by solving the problem in some lower dimensional subspace without using level-set methods.

A coarse-to-fine boundary location with a self-organizing map (SOM)-like method is proposed in this paper. Inspired from the conventional SOM and universal gravitation, given a small quantity of supervision seeds from the desired boundaries, neurons are used to evolve to the desired boundaries in a coarse-to-fine framework. The major components of this framework are the designs of union action and evolving rate. In the course of neuron evolution, the union actions acting on these neurons will offer them the evolving directions. Also controlled by the corresponding referenced gradients, the neurons' evolving rates are adaptively adjusted at different positions. With the union actions and evolving rates, the neurons will evolve with appropriate manners to expand the set of feature points on the desired boundaries. The newly expanded feature points will cause the generation updates for feature points and neurons, and offer new information to guide the new generation of neurons to the boundaries. What is more, the proposed multiround evolution is as well a coarse-to-fine way for boundary location. Experiments and comparisons show that the proposed method performs well in complex long concavities, inhomogeneous and weak boundary location with good initialization flexibility.

A novel method to reconstruct object boundaries with geodesic circular arc is proposed in this paper. Within this framework, an energy of circular arc spline is utilized to simultaneously arrange and interpolate each member in the set of sparse unorganized feature points from the desired boundaries. A general form for a family of parametric circular arc spline is firstly derived and followed by a novel method of arranging these feature points by minimizing an energy term depending on the circular arc spline configuration defined on these feature points. With regard to the fact that the energy function is usually nonconvex and nondifferentiable at its critical points, an improved scheme of particle swarm optimizer is given to find the minimum for the energy in this paper. With this improved scheme, each pair of neighboring feature points along the boundaries of the desired objects are picked out from the set of sparse unorganized feature points, and the corresponding directional chord tangent angles are computed simultaneously to finish interpolation. We show experimentally and comparatively that the proposed method can perform effectively to restrict leakage on weak boundaries and premature convergence on long concave boundaries. Besides, it has good noise robustness and can as well extract multiple and open boundaries.