Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up-and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture; it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://github.com/lalalulao/MWT-Diff.

False correspondence removal is a persistent challenge in image feature-matching-based applications, especially in complex scenes. Traditional methods often rely on the consistency assumption to model the motion of correct correspondences, which neglects non-consistent correct correspondences, resulting in suboptimal performance. In this paper, we introduce the NCNet, a novel network designed to address this limitation by fitting the motion of both consistent and non-consistent correct correspondences using the learnable frequency response function. Unlike conventional approaches that focus on pixel-based 2D movements, NCNet utilizes the Motion Fitting Residual Module to estimate the high-dimensional motion, capturing the intricate 3D geometrical variations of matching pairs. To further enhance performance, we propose a loss function that balances the performance between the two types of correct correspondences. Extensive experiments demonstrate that NCNet significantly outperforms existing methods in terms of precision for false correspondence removal. The implementation of our approach is publicly available at: https://github.com/Livsdjo/NCNet-Code.

Low-light image enhancement (LLIE) has long been a challenging problem due to the complexities of noise and illumination variations in low-light environments. In this paper, we propose an innovative method that combines Retinex theory and diffusion models to enhance low-light images. Our method decomposes the input image into reflectance and illumination components, where the reflectance is denoised using an adaptive diffusion-based reflectance denoising module, and the illumination is enhanced via a newly designed illumination enhancement module. Notably, we introduce an adaptive noise estimation network to guide the denoising process of the diffusion model, effectively removing noise while preserving image details. Extensive experiments on multiple benchmark datasets demonstrate that our method significantly outperforms existing techniques in terms of PSNR and SSIM, while reducing artifacts and preserving natural colors.

Accurate activation and optimization of autofocus (AF) functions are essential for capturing high-quality images and minimizing camera response time. Traditional contrast detection autofocus (CDAF) methods suffer from a trade-off between accuracy and robustness, while learning-based methods often incur high spatio-temporal computational costs. To address these issues, we propose a lightweight focus discriminative network (FDNet) tailored for AF tasks. Built upon the ShuffleNet V2 backbone, FDNet leverages a genetic algorithm optimization (GAO) strategy to automatically search for efficient network structures, and incorporates coordinate attention (CA) and multi-scale feature fusion (MFF) modules to enhance spatial, directional, and contextual feature extraction. A dedicated focus stack dataset is constructed with high-quality annotations to support training and evaluation. Experimental results show that FDNet outperforms mainstream methods by up to 4% in classification accuracy while requiring only 0.2 GFLOPs, 0.5 M parameters, a model size of 2.1 MB, and an inference time of 0.06 s, achieving a superior balance between performance and efficiency. Ablation studies further confirm the effectiveness of the GAO, CA, and MFF components in improving the accuracy and robustness of focus feature classification.

Whiteboard collaboration in virtual reality (VR) is an important task in collaborative virtual environments. The current research mainly relies on the use of controllers or dedicated pens but additional devices will cause inconvenience to users. Bare-hand writing offers rich collaborative semantics through natural gestures but remains underexplored. This paper addresses challenges and solutions for bare-hand whiteboard collaboration. We analyze the input process and identify key challenges in determining pen-drop, writing, and pen-lift intentions while maintaining user control over their avatar. Our approach addresses two VR scenarios: one without and one with physical planes. The method for the first case is called Air-writing, which dynamically adjusts the distance between the avatar's torso and the virtual whiteboard during the processes of pen-drop and pen-lift to ensure a consistent writing experience in VR. The method for the second case is called Physical-writing, which allows users to write smoothly with passive haptic feedback and physical constraints provided by the real surface by remapping the whiteboard in VR with a plane in reality. A comprehensive user study is conducted to evaluate communication efficiency, input accuracy, collaboration efficiency, and user experience of the two methods. The experimental results indicate that bare-hand interaction improves communication efficiency by 8% over controllers and performs similarly to real-world whiteboard collaboration. The Physical-writing method also demonstrates higher accuracy and user satisfaction compared to the Air-writing method.

In recent years, low-light image enhancement techniques have made significant progress in generating reasonable visual details. However, current methods have not yet fully utilized the full semantic prior of visual elements in low-light environments. Therefore, images generated by these low-light image enhancement methods often suffer from degraded visual quality and may even be distorted. To address this problem, we propose a method to guide low-light image enhancement by predicting semantic priors. Specifically, we train a semantic prior predictor under standard lighting conditions, which is made to learn and predict semantic prior features for low-light images by knowledge distillation on high-quality standard images. Subsequently, we utilize a semantic-aware module that enables the model to adaptively integrate these learned semantic priors, thus ensuring semantic consistency of the enhanced images. Experiments show that the method outperforms several current state-of-the-art methods in terms of visual performance on the LOL-v2 and SICE benchmark datasets. Our code is available athttps://github.com/tianzhiya/SELL.

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results.

Infrared and visible image fusion is currently an important research direction in the field of multimodal image fusion, which aims to utilize the complementary information between infrared images and visible images to generate a new image containing richer information. In recent years, many deep learning-based methods for infrared and visible image fusion have emerged.However, most of these approaches ignore the importance of semantic information in image fusion, resulting in the generation of fused images that do not perform well enough in human visual perception and advanced visual tasks.To address this problem, we propose a semantic prior knowledge-driven infrared and visible image fusion method. The method utilizes a pre-trained semantic segmentation model to acquire semantic information of infrared and visible images, and drives the fusion process of infrared and visible images through semantic feature perception module and semantic feature embedding module.Meanwhile, we divide the fused image into each category block and consider them as components, and utilize the regional semantic adversarial loss to enhance the adversarial network generation ability in different regions, thus improving the quality of the fused image.Through extensive experiments on widely used datasets, the results show that our approach outperforms current leading algorithms in both human eye visualization and advanced visual tasks.

Accurately computing optical flow in low-contrast and noisy dark images is challenging, especially when contour information is degraded or difficult to extract. This paper proposes CEDFlow, a latent space contour enhancement for estimating optical flow in dark environments. By leveraging spatial frequency feature decomposition, CEDFlow effectively encodes local and global motion features. Importantly, we introduce the 2nd-order Gaussian difference operation to select salient contour features in the latent space precisely. It is specifically designed for large-scale contour components essential in dark optical flow estimation. Experimental results on the FCDN and VBOF datasets demonstrate that CEDFlow outperforms state-of-the-art methods in terms of the EPE index and produces more accurate and robust flow estimation. Our code is available at: https://github.com/xautstuzfy.

Infrared and visible image fusion is an important multimodal image processing task that aims to enhance computer vision performance by effectively fusing infrared and visible images. Although in recent years, many deep learning-based methods for infrared and visible image fusion have emerged. Howeve, most of these methods ignore the important role of semantic information in image fusion. Therefore, this paper proposes a semantic priori guided infrared and visible image fusion network called SPGFusion. It uses an adversarial generative network framework based on semantic priors to guide the infrared and visible image fusion process by combining a semantic feature-aware module and semantic generative adversarial loss. Experimental results demonstrate that the SPG-Fusion method yields more visually appealing fusion results and outperform state-of-the-art image fusion algorithms in visual quality and quantitative evaluation. The source code is available at https://github.com/tianzhiya/SPGFusion.

Photographs taken in low-light conditions have a low signal-to-noise ratio and impaired visual quality. We observe that low-light images exhibit a lower signal-to-noise ratio, resulting in a mixture of fine details, textures, and noise, making it challenging to reconstruct small-scale textures in the image. Inspired by this observation, we propose a SNR-guided CNN-Transformer network for high frequency restoration during low light image enhancement. The proposed method first decomposes image into high-frequency and low frequency components by image decomposition module. The low-frequency image is processed by a trainable Low Frequency SNR Perception (LFSP) module, resulting in excellent denoising performance and generating SNR-enhanced images with clearer edge contours. Guided by the low-frequency SNR feature maps, the details and textures of the high-frequency components are enhanced using a combination of transformer networks and convolutional networks, thereby compensating the detail distortions in the high frequency components of the image. The subjective and objective experiments demonstrate that our proposed method outperforms existing approaches in terms of detail and structure preservation.

Dark Image Enhancement (DIE) aims to improve contrast and restore details for captured images under low illumination. Currently, traditional Transformer methods have achieved significant performance in the DIE problem; however, all-pairs correlation computation is redundant in learning key properties and restoring high-order representations. To alleviate this problem, we introduce a Channel-wise Guidance Sparse Transformer framework, namely CGSformer, which not only adaptively selects the key channel-wise representations through a threshold operator, but also keeps the most useful self-attention values for feature restoration guided by the selected information. Besides, we introduce a Bidirectional Gate Feed-Forward (BGFF) network to aggregate features to better facilitate high-quality image reconstruction. The experiments are conducted on representative datasets, showing that our CGSformer consistently achieves state-of-the-art performance on widely used benchmarks.

Deep learning models have demonstrated excellent performance for polarimetric SAR image classification. However, existing approaches generally use a fixed square window to sample image blocks as the network input, which may not effectively extract various terrain objects. To alleviate this issue, we proposed an adaptive region sampling network to learn different terrain types by introducing a novel sampling scheme with varying direction and scale. Initially, a complex PolSAR image is segmented into homogeneous, heterogeneous and boundary regions. Subsequently, small-scale and large-scale sampling windows are designed for homogeneous and heterogeneous regions, to capture local and global features for two types of regions respectively. Additionally, an adaptive directional sampling window is designed for boundary regions to ensure context consistency in the image block and prevent edge confusion. Experiments conducted on real PolSAR data sets demonstrate that our method achieves superior classification results, providing both regional consistency and boundary preservation.

Deep learning models have been widely applied to Polarimetric Synthetic Aperture Radar (PolSAR) image classification due to their excellent performance. However, unlike natural images, PolSAR data is a 3×3 covariance matrix for each resolution unit. Existing deep learning methods generally convert the covariance matrix into a vector as the input of neural networks, which destroys the correlation between channels and distorts the matrix structure. To alleviate this issue, we explore a Symmetric Positive Definite (SPD) convolution network for PolSAR images, which directly inputs the PolSAR complex matrix into the network to learn the geometric features in Riemannian space. Furthermore, a CNN-enhanced SPDnet is designed to further learn the contextual high-level features, which can convert Riemannian matrix features into Euclidean space and apply them for classification. Experimental results on real PolSAR data sets demonstrate the proposed method can achieve better performance than the state-of-the-art methods.

In the low light image enhancement, single exposure images contains a limited dynamic range, which hinders the restoration of contrast and texture. To address these problems, we propose a multi exposure generation and fusion method (MEGF) which simulates multi exposure images and perform feature fusion and enhancement on these images. First, we propose a Multi-Exposure Generation (MEG) method, which constructs the Gaussian Distribution for each exposure level based on multi exposure datasets. MEG can generate images with different exposure levels based on the constructed distribution. Then, the Perceptual Importance based Multi-Exposure Feature Enhancement (PIMEFE) block is developed to fuse the feature of generated multi exposure images using VGG-16 network. Before fusion, the generated images are input to Multi Scale Recursive Feature Enhancement (MSRFE) blocks and obtain the denoised and enhanced features. Finally, the fused feature are input to Curve Adjustment (CA) block for fine tuning and provide the color enhancement on fusion features. We propose the Multiple Exposure Recursive Fusion (MERF) block which estimates the adjusting factors for CA block. Experimental results demonstrate that our method outperforms other techniques in both subjective and objective evaluations on real and synthetic datasets.

Photographs taken in low-illumination environment have a low signal-to-noise ratio and impaired visual quality. Enhancing lowlight images tends to amplify noise. To address this problem, we propose a Multi-Scale Recursive Feature Enhancement (MSRFE) network for low light image enhancement. The MSRFE network consists of several Feature Enhancement (FE) blocks which are applied to enhance the multi-scale image feature and remove the noise recursively in each scale residual map between adjacent scale feature. Then, a deep recursive Curve Adjustment (CA) block is proposed further fine-tunes the output of MSRFE netowrk by learning a non-linear curve which can adjust the image luminance and details. We evaluate the proposed method on both real and synthetic datasets. The results show that our proposed method outperforms other state-of-the-art methods on both visual and objective evaluation indicators.

In low-light images, contrast and brightness are corrupted, making it difficult to accurately percept detail and edge information with the naked eye. Because of the development of multi sensor imaging, RGB-IR image fusion can enhance the imaging quality in low light conditions. However, the existing fusion algorithms have insufficient enhancement, distorted detail and low contrast, which make it difficult to generate high-quality fusion results. In this paper, we propose a Transformer-CNN image fusion method which considers global-local features fusion for low light image enhancement. The ConvGRU module is developed to alternatively select the global and local features with Transformer and CNN network. To effectively improve the network performance, a learnable weight adaptive loss function is proposed to adjust the weight of loss functions during training. Numerous experiments prove that our method can enrich fusion image information, improve image contrast and edge in low-light scenes compared to state of the art methods.

False correspondence removal remains a challenge for many feature-matching-based applications. This paper proposes a solution that revisits the local semantic context via position-attentive learning. First, a cross-divisional module is introduced to extract semantic features from image-patch pairs. Then, through a position-attentive mechanism, the parametric positions and extracted semantic features are jointly utilized to compute the probabilities of a set of putative correspondences. The proposed approach is evaluated on several indoor and outdoor challenging datasets, containing up to 70%+ false correspondences. In most cases, the solution outperforms existing algorithms in terms of matching precision and F1-score. Furthermore, an ablation study indicates that revisiting the semantic context improves precision by nearly 5%. The code is available at the link below1.

In the low-light conditions, images are corrupted by low contrast and severe noise, but event cameras can capture event streams with clear edge structures. Therefore, we propose an Event-Guided Attention Network for Low-light Image Enhancement (EGAN) using a dual branch Network and recover clear structure with the guide of events. To overcome the lack of paired training datasets, we first synthesize the dataset containing low-light event streams, low-light images, and the ground truth (GT) normal-light images. Then, we develop an end-to-end dual branch network consisting of a Image Enhancement Branch (IEB) and a Gradient Reconstruction Branch (GRB). The GRB branch reconstructs image gradients using events, and the IEB enhances low-light images using reconstructed gradients. Moreover, we develops the Attention based Event-Image Feature Fusion Module (AEIFFM) which selectively fuses the event and low-light image features using the spatial and channel attention mechanism, and the fused features are concatenated into the IEB and GRB, which respectively generate the enhanced images with clear structure and more accurate gradient images. Extensive experiments on synthetic and real datasets demonstrate that the proposed EGAN produces visually more appealing enhancement images, and achieves a good performance in structure preservation and denoising over state-of-the-arts.

To facilitate newcomer onboarding, GitHub recommends the use of "good first issue" (GFI) labels to signal issues suitable for newcomers to resolve. However, previous research shows that manually labeled GFIs are scarce and inappropriate, showing a need for automated recommendations. In this paper, we present GFI-Bot (accessible at https://gfibot.io), a proof-of-concept machine learning powered bot for automated GFI recommendation in practice. Project maintainers can configure GFI-Bot to discover and label possible GFIs so that newcomers can easily locate issues for making their first contributions. GFI-Bot also provides a high-quality, up-to-date dataset for advancing GFI recommendation research.

Low light images suffer from a low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint contrast enhancement and noise reduction of low light images via just-noticeable-difference (JND) transform. We adopt the JND transform to achieve both contrast enhancement and noise reduction based on human visual perception. First, we generate a JND map based on an the human visual system (HVS) response model from foreground and background luminance, called JND transform. Second, for base image, we perform perceptual contrast enhancement based on luminance adaptation to effectively allocate a dynamic range to each gray level while preventing under enhancement (tone distortion) and over-enhancement. Third, we refine the JND map using Weber's law, luminance adaptation and visual masking. Weber's law enhances the JND map based on the luminance variation after contrast enhancement. Luminance adaptation suppresses noise for smooth regions, while visual masking enforces detail enhancement for textural regions. Fourth, we perform inverse JND transform to generate the enhanced luma channel from the JND map and base image. Finally, we conduct chroma denoising by transferring texture information of the enhanced luma channel to the chroma channels with guided filtering. Experimental results show that the proposed method achieves both contrast enhancement and noise reduction for low light images as well as outperforms state-of-the-art methods in terms of quantitative measurements.

Low light images suffer from low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint enhancement and denoising of low light images via just-noticeable-difference (JND) transform. We achieve contrast enhancement and noise reduction simultaneously based on human visual perception. First, we perform contrast enhancement based on perceptual histogram to effectively allocate a dynamic range while preventing over-enhancement. Second, we generate JND map based on an HVS response model from foreground and background luminance, called JND transform. Then, we refine JND map using Weber's law and visual masking. Weber's law enhances the JND map based on the luminance variation after enhancement, while visual masking provides noise suppression for smooth regions and detail enhancement for texture regions. Finally, we conduct chroma denoising that transfers texture information of the denoised luma channel to the chroma channels by guided image filtering. Experimental results show that the proposed method achieves good performance in contrast enhancement and noise reduction while successfully preserving details.

In this paper, we propose multi-spectral fusion and denoising (MFD) of RGB and NIR images using multi-scale wavelet analysis. We formulate MFD of RGB and NIR images as a maximum a posterior (MAP) estimation problem in the wavelet domain. The direct fusion of noisy RGB and NIR image often leads to contrast attenuation due to the discrepancy between RGB and NIR images. Thus, we generate the wavelet scale map for fusion and denoising based on correlation between NIR and RGB wavelet coefficients. To consider local contrast and visibility of NIR data on RGB components, we provide the contrast preservation term for scale map estimation based on the local contrast and visibility. We use the regularization term to select high visibility and contrast of NIR wavelet coefficients in the scale map. Since noise generally appears in the high frequency band, we use gradients of NIR wavelet coefficients as the weight for weighted least square (WLS) smoothing in the scale map. Based on the wavlet scale map, we perform fusion and denoising of RGB and NIR wavelet coefficients. Experimental results show that the proposed method successfully performs fusion of RGB and NIR images with noise reduction and detail preservation as well as outperforms state-of-the-arts in terms of discrete entropy (DE) and feature-based blind image quality evaluator (FBIQE).

The rapid advancements in the Internet of Things (IoT) and cloud computing technologies have significantly promoted the collection and sharing of various data. In order to reduce the communication cost and the storage overhead, it is necessary to exploit data deduplication mechanisms. However, existing data deduplication technologies still suffer security and efficiency drawbacks. In this paper, we propose two secure data deduplication schemes based on Rabin fingerprinting over wireless sensing data in cloud computing. The first scheme is based on deterministic tags and the other one adopts random tags. The proposed schemes realize data deduplication before the data is outsourced to the cloud storage server, and hence both the communication cost and the computation cost are reduced. In particular, variable-size block-level deduplication is enabled based on the technique of Rabin fingerprinting which generates data blocks based on the content of the data. Before outsourcing data to the cloud, users encrypt the data based on convergent encryption technologies, which protects the data from being accessed by unauthorized users. Our security analysis shows that the proposed schemes are secure against offline brute-force dictionary attacks. In addition, the random tag makes the second scheme more reliable. Extensive experimental results indicate that the proposed data deduplication schemes are efficient in terms of the deduplication rate, the system operation time, and the tag generation time.

Image quality in mobile displays is considerably influenced by ambient light. In the daylight condition, images on mobile displays are darkly perceived by the human visual system due to the limited dynamic range of display, which causes loss of luminance and details. In this paper, we propose readability enhancement of displayed images under ambient light by enhancing both luminance and details. We design a weighted optimization framework, which contains the data term for luminance enhancement and the gradient term for detail enhancement. In the data term, we use an ambient light nonlinear intensity-transfer function considering display properties, ambient light, and image contents. In the gradient term, we employ a threshold versus intensity adaptation function based on the degree of ambient light adaptation to compensate for gradient distortions. We solve the optimization framework using a numerical solver and achieve image enhancement in displayed images. Experimental results demonstrate that the proposed method significantly improves readability of mobile displays under ambient light by enhancing luminance and details of images.

In low light condition, the signal-to-noise ratio (SNR) is low and thus the captured images are seriously degraded by noise. Since low light images contain much noise in flat and dark regions, contrast enhancement without considering noise characteristics causes serious noise amplification. In this paper, we propose low light image enhancement based on two-step noise suppression. First, we perform noise aware contrast enhancement using noise level function (NLF). NLF is used to get a noise aware histogram which prevents noise amplification, and we use the noise aware histogram in contrast enhancement. However, the increase of intensity by contrast enhancement reduces the visibility threshold, which makes noise visible by human eyes. Second, we utilize a just noticeable difference (JND) model from luminance adaptation to suppress noise based on human visual perception. Experimental results show that the proposed method successfully enhances contrast in low light images while minimizing noise amplification.

Image quality of mobile displays are significantly influenced by ambient light. In the daylight condition, displayed images on mobile displays are darkly perceived by human visual system (HVS), which suffer from significant detail loss. However, only luminance enhancement seriously affects image details especially for bright regions. To overcome this problem, we propose a quadratic optimization framework which includes data term for luminance enhancement and gradient term for detail enhancement. In the data term, we provide an ambient light nonlinear intensity-transfer function for adaptive luminance enhancement depending on display properties, ambient light, and image contents. In the gradient term, Weber's law is employed for detail enhancement. Finally, we achieve both luminance and detail enhancement by solving the optimization framework. Experimental results demonstrate that the proposed method remarkably enhances the visibility of displayed images under strong ambient light.