The semantic matching problem consists of recognizing if the candidate text is relevant to a particular input text. Semantic similarities can be determined from human-curated knowledge, but such knowledge may not be available in every language. Instead, statistical learning techniques have been applied, but these techniques circumvent the need for manual feature engineering by using large datasets to train models to perform semantic similarity scoring between portions of text or words. The pre-trained transformer provides a further mechanism to consolidate the information throughout a sentence into single sentence-level representations, but these representations may not be optimal for the matching task. As an alternative, we propose an interactive semantic transformer based on a greedy layer-wise framework to learn a distributed similarity representation for sentence pairs. The novelty of the architecture lies in an abstract representation of the semantic similarities created by three-stage learning strategies. Model training is accomplished through a greedy layer-wise training scheme, that incorporates both supervised and unsupervised learning. The proposed model is experimentally compared to state-of-the-art approaches on three different dataset types: the library TREC, the Yahoo!, and Stack Exchange community question datasets, and results show the proposed model outperforming other approaches.

Detecting infrared dim and small targets is one crucial step for many tasks such as early warning. It remains a continuing challenge since characteristics of infrared small targets, usually represented by only a few pixels, are generally not salient. Despite that many traditional methods have significantly advanced the community, their robustness or efficiency is still lacking. Most recently, CNN-based object detection has achieved remarkable performance and some researchers focus on it. However, these methods are not computationally efficient when implemented on some CPU-only machines and few datasets are available publicly. To promote the detection of infrared small targets in complex backgrounds, we propose a new lightweight CNN-based architecture. The network contains three modules: the feature extraction module is designed for representing multi-scale and multi-level features, the grid resample operation module is proposed to fuse features from all scales, and a decoupled head to distinguish infrared small targets from backgrounds. Moreover, we collect a brand-new infrared small target detection dedicated dataset which consists of 68311 practical captured images with complex backgrounds for alleviating the data dilemma. To validate the proposed model, 54758 images are used for training and 13553 images are used for testing respectively. Extensive experimental results demonstrate that the proposed method outperforms all traditional methods by a large margin and runs much faster than other CNN methods with high precision. The proposed model can be implemented on the Intel i7-10850H CPU (2.3GHz) platform and Jetson Nano for real-time infrared small target detection at 44 FPS and 27 FPS, respectively. It can be even deployed on an Atom x5-Z8500 (1.44GHz) machine at about 25 FPS with 128×128 local images. The source codes and the dataset have been made publicly available at https://github.com/SeaHifly/Infrared-Small-Target.

Remotely sensed images degraded by additive white Gaussian noise (AWGN) are not beneficial for the analysis of their contents. Such a phenomenon is usually modeled as an inverse problem which can be solved by model-based optimization methods or discriminative learning approaches. The former pursue their pleasing performance at the cost of a highly computational burden while the latter are impressive for their fast testing speed but are limited by their application range. To join their merits, this letter proposes a nonlocal self-similar (NSS) block-based deep image denoising scheme, namely deep low-rank prior (DLRP), which includes the following key points: First, the low-rank property of the neighboring NSS patches ordered lexicographically is utilized to model a global objective function (GOF). Second, with the aid of an alternative iteration strategy, the GOF can be easily decomposed into two independent subproblems. One is a quadratic optimization problem, and has a closed-form solution. While the other is a low-rank minimization denoising problem and is learned by deep convolutional neural network (DCNN). Then, the deep denoiser, acted as a modular part, is plugged into the model-based optimization method with adaptive noise level estimation to solve the inverse problem. In the experiments, we first discuss parameter setting and the convergence. Then, quantitative/qualitative comparisons of experimental results validate that the DLRP is a flexible and powerful denoising method to achieve competitive performance which even outperforms those produced by state-of-the-arts.

Low/high or uneven luminance results in low contrast of remotely sensed images (RSIs), which makes it challenging to analyze their contents. In order to improve the contrast and preserving fine weak details of RSIs, this letter proposes a novel enhancement framework to correct luminance guided by weighted least squares (WLS), including the following key parts. First, an image is separated into a base layer and a detail layer by employing the WLS. Then, a learning network is proposed to correct luminance for the base layer enhancement. Next, an enhancement operator for improving the detail layer is computed by using the original image and the enhanced base layer. Finally, the output image is obtained with a fusion of the enhanced base and detail components. Both quantitatively and qualitatively experimental results verify that the proposed method performs better than the state of the arts in contrast improvement and detail preservation.

In the above article[1], the model in(1)should be revised as\begin{equation*}\min _{\mathcal{I}^{\mathcal{B}}}\left\{\left(\mathcal{I}-\mathcal{I}^{\mathcal{B}}\right)^{2}+\lambda\left(a_{x}(\mathcal{I})\left(\frac{\partial \mathcal{I}^{\mathcal{B}}}{\partial x}\right)^{2}+a_{y}(\mathcal{I})\left(\frac{\partial \mathcal{I}^{\mathcal{B}}}{\partial y}\right)^{2}\right)\right\} \end{equation*}to be minimized for${\mathcal {I}}^{\mathcal {B}}$.

Stripe and random noise are two different degradation phenomena that commonly coexist in optical remote sensing images, and they are often modeled as inverse problems. In model-based inverse problems, analysis and synthesis sparse representations (SSRs) are used as regularization terms to obtain approximate solutions due to their respective merits, i.e., the nonzero coefficients in SSR are usually used to describe an image, while the indexes of zeros in analysis sparse representation (ASR) are used to characterize the stripe. Inspired by these merits, we propose a unified variational framework, called a joint analysis and weighted synthesis (JAWS) sparsity model, to simultaneously separate the clean image and the stripe from a single optical remote sensing image. To solve the JAWS sparsity model efficiently, an alternating minimization optimization strategy is first employed to separate it into two subproblems that are used for different tasks. One called as weighted SSR (WSSR) is the main for optical remote sensing image denoising, which can be effectively solved by employing the weighted singular value thresholding operator, while the other called as ASR is the main approach for optical remote sensing image destriping, which is optimized by adopting the split Bregman iteration. By minimizing the two subproblems alternatively, the proposed JAWS sparsity model is efficiently solved. Finally, both quantitative and qualitative results of experiments on synthetic and real-world optical remote sensing images validate that the proposed approach is effective and even better than the state of the arts.

Real-time image lossless and near-lossless compression based on LOCO-I algorithm is in great demand in many critical missions, such as satellite remote sensing, space exploration, and nuclear medical imaging, for its excellent complexity/compression rate tradeoff. However, the real-time implementation of LOCO-I encounters two bottlenecks in error prediction: the context conflict in context update and the pixel reconstruction loop in near-lossless mode, which always limit the overall throughput in practice. This paper adopts a patch-wise compression method and proposes a high-performance globally pipelined hardware architecture with spatial parallelism in local, in which patch-wise dual parallel error prediction (PEP) modules are designed. The PEP gains an extra pixel cycle and thus, alleviates both the two bottlenecks. Besides, an equivalent simplification scheme is designed to accelerate the error quantization computation, the most time and resource consuming procedure in the pixel reconstruction loop, saving the resource usage, and reducing computing delay. The proposed encoder is able to compress each image patch independently in either lossless or near-lossless mode by parameter setting. It facilitates the Region of Interests compression so as to improve the overall compression ratio and achieves excellent information fidelity. Moreover, the possible error propagation can be prevented and constrained within one single patch. The proposed architecture is implemented on a XILINX Virtex6-75t FPGA and achieves a maximum throughput up to 51.684 MPixel/s. It is the fastest architecture reported in the literature which implements both lossless and near-lossless compression modes.

Non‐local self‐similar patch‐based denoising techniques have been viewed as the most popular denoising approaches in computer vision. This study has proposed a novel iterative weighted sparse representation (IWSR) scheme for X‐ray cardiovascular angiogram image denoising. The main procedures of this scheme include four parts. First, a maximum a posterior (MAP) distribution by the Bayes’ theory is adopted to simultaneously estimate the estimated image and sparse representation with different Gaussian distributions approximating to likelihood prior, non‐local self‐similar patch prior and sparse representation prior. Second, the MAP problem is converted to minimise an energy function using the logarithmic transformation. Third, the function is efficiently solved by the single and effective alternating directions method of multipliers algorithm along with singular value decomposition (SVD) algorithm. Finally, owing to learned dictionary by K‐SVD algorithm, the qualitative and quantitative results of widely synthetic experiments demonstrate that the proposed IWSR denoising method performs effectively and can obtain competitive denoising performance and high‐quality images compared with those advanced denoising methods. The results of extensive experiments on clinical X‐ray angiogram images further illustrate that the IWSR method performs well on noise reduction and vascular structures including edges and capillaries preservation, integral cardiovascular trees of which are beneficial for clinicians to diagnose and analyse cardiovascular diseases.

Enhancement and denoising have always been a pair of conflicting problems in image processing of computer vision. Inspired by an earlier dual-domain filter (DDF), this letter proposes a progressive DDF to simultaneously enhance and denoise low-quality optical remote-sensing images. The main procedure of the proposed enhancement filter has two parts. First, a bilateral filter is exploited as a guide filter to obtain high-contrast images, which are enhanced by a histogram modification method. Then, low-contrast useful structures are restored by a short-time Fourier transform and are enhanced using an adaptive correction parameter. Both the quantitative and qualitative results of experiments on synthetic and real-world low-quality remote-sensing images demonstrate that the proposed method performs well on contrast enhancement, structure preservation, and noise reduction. Moreover, its satisfactory computation time resulting from its simple implementation makes it suitable for extensive application.

Atmospheric turbulence affects imaging systems by virtue of wave propagation through a medium with a nonuniform index of refraction. It can lead to blurring in images acquired from a long distance away. In this letter, it is observed that blurring increases the second-order central moment (SOCM) of images, and we introduce a new parametric blur identification method by minimizing SOCM. The method applies to finite-support images, in which the scene consists of a finite-extent object against a uniformly black, gray, or white background. The SOCM method has been validated by direct comparisons with other methods on simulated and real degraded images.

A novel method is proposed to deal with the problem of building recognition in forward-looking infrared (FLIR) image sequences. Two computational models are deduced in this paper: one is the perspective transformation model, through which an image is transformed perspectively from downward-looking state to forward-looking state; the other is the indirect location model, through which the position of a building is computed in an FLIR image. In addition, in order to illustrate the application scope of our method, the error analysis is presented. The proposed approach is validated by extensive experiments, with images taken in different weather conditions, seasons and at different times. Superior recognition results were obtained on three FLIR image sequences we collected.

In this paper, an entropic thresholding method based on the gray-level spatial correlation (GLSC) histogram defined by ourselves is presented. Compared with traditional two-dimensional histogram, we take into account the image local property in a different way by GLSC histogram. In experiment, we make comparison of the proposed method with two-dimensional entropic thresholding method proposed by Abutaleb and one-dimensional entropic thresholding method proposed by Kapur. The experiment demonstrates that generally our method could yield equivalent or even better result than Abutaleb’s method while saving time remarkably and perform much better than Kapur’s method without too more time consumption.

This paper discusses the research in small target detection in infrared images with heavy clutter background. For most infrared images, ship objects are rather dim in the relative dark sea surface background. The existence of scan line disturbance and noise also increases the difficulty in proper detection. Dim objects must be distinguished from a dark background. On the other hand, the small targets must also be distinguished from clutters. Through analysis of the targets and background, we build characteristic models of small ship objects, noise and sea backgrounds respectively, and indicate their differences in spatial and frequency domains among them. Based on the principles of signal processing, pattern recognition and artificial intelligence, we propose a combined algorithm for detecting sea surface small targets. In this algorithm, components of background and noise are first suppressed by a multilevel filter designed accordingly, meanwhile enhancing the target ones of interest. The pixels of the candidate targets are then discriminated by minimum risk Bayes test. Finally, according to a priori knowledge about the targets such as the ranges of their sizes, the targets of interest can be detected. In particular, the related probability distributions used by statistic decision are obtained by offline learning of typical training samples. Experiments show that the algorithm is excellent for such kinds of target detection and is robust to noise.

This paper presents a new approach to deal with the translation- and scale-invariant problem of the discrete wavelet transform (DWT). Using a signal-dependent filter, whose impulse response is calculated by the first two moments of the original signal and a scale function of an orthonormal wavelet, we adaptively renormalized a signal. The renormalized signal is then decomposed by using the algorithm of the conventional DWT. The final wavelet transform coefficients, called adaptive wavelet invariant moments (AWIM), are proved to be both translation- and scale-invariant. Furthermore, as an application, we define a new textural feature in the framework of our adaptive wavelet decomposition, show its stability to shift and scaling, and demonstrate its efficiency for the task of scale-invariant texture identification.

This correspondence is concerned with a method for image segmentation on the visual principle. The inconsistency between the conventional discriminating criterion and the human vision mechanism in perceiving an object and its background is analyzed and an improved discriminating criterion with visual nonlinearity is defined. A new model and an algorithm for image segmentation calculation are proposed based on the spatially adaptive principle of human vision and the relevant hypotheses about object recognition. This is a two-stage process of image segmentation. First, initial segmentation is realized with the bottom-up segmenting algorithm, followed by the goal-driven segmenting algorithm to improve the segmentation results concerning certain regions of interest. Experimental results show that, compared with some conventional and gradient-based segmenting methods, the new method has the excellent performance of extracting small objects from the images of natural scenes with a complicated background.

This paper deals with the adaptive image matching and displacement estimation problems of dissimilar sequential images. Adaptive matching may be referred to a kind of functional optimization. Based on the additive measuring model for sequential images with gray-level deviation and the finite element technique, a class of gray-level correction model and robust matching criterion function are proposed, and an adaptive hierarchical searching algorithm is studied, which can automatically evaluate the temporal and spatial gray-level deviation existed in sequential images and filter out the interference thereof to increase the probability of correct estimation of displacement. Experimental results for the estimation of displacement fields with translation and/or small angle rotation in medical digital subtraction angiography (DSA) imaging and aerial remote sensing pictures are presented.>

It is shown that the statistical properties of the mean absolute difference mismatch measure surface is dependent on the autocorrelation function of image and signal-to-noise ratio. A method for finding the match point is presented, based on the idea of testing a set of hypotheses. Using the threshold estimation formulas and the optimized search scheme developed by the authors, a fast algorithm which may be adaptable to various expected operating conditions can be implemented.>