Sparse representation models code an image patch as a linear combination of few atoms chosen out from over-complete dictionary, and they have shown promising results in various restoration applications. However, due to the degradation observed (e.g., noisy, blurred, and/or down-sampled), sparse representations by conventional may not be accurate enough for faithful reconstruction original image. To improve performance representation-based restoration, this paper concept coding noise is...

Simultaneous sparse coding (SSC) or nonlocal image representation has shown great potential in various low-level vision tasks, leading to several state-of-the-art restoration techniques, including BM3D and LSSC. However, it still lacks a physically plausible explanation about why SSC is better model than conventional for the class of natural images. Meanwhile, problem sparsity optimization, especially when tangled with dictionary learning, computationally difficult solve. In this paper, we...

Sparsity has been widely exploited for exact reconstruction of a signal from small number random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful techniques in various compressed sensing (CS) studies. In this paper, we propose nonlocal low-rank regularization (NLR) approach toward exploiting and explore its application into CS both photographic MRI images. We also the use nonconvex log det ( X) as smooth surrogate function rank...

Temporal action proposal generation is an challenging and promising task which aims to locate temporal regions in real-world videos where or event may occur. Current bottom-up methods can generate proposals with precise boundary, but cannot efficiently adequately reliable confidence scores for retrieving proposals. To address these difficulties, we introduce the Boundary-Matching (BM) mechanism evaluate of densely distributed proposals, denote a as matching pair starting ending boundaries...

Hyperspectral imaging has many applications from agriculture and astronomy to surveillance mineralogy. However, it is often challenging obtain high-resolution (HR) hyperspectral images using existing techniques due various hardware limitations. In this paper, we propose a new image super-resolution method low-resolution (LR) HR reference of the same scene. The estimation formulated as joint dictionary sparse codes based on prior knowledge spatial-spectral sparsity image. representing...

Where does the sparsity in image signals come from? Local and nonlocal models have supplied complementary views toward regularity natural images - former attempts to construct or learn a dictionary of basis functions that promotes sparsity; while latter connects with self-similarity source by clustering. In this paper, we present variational framework for unifying above two propose new denoising algorithm built upon clustering-based sparse representation (CSR). Inspired success l <sub...

Single sensor digital color cameras capture only one of the three primary colors at each pixel and a process called demosaicking (CDM) is used to reconstruct full images. Most CDM algorithms assume existence high local spectral redundancy in estimating missing samples. However, for images with sharp transitions saturation, such an assumption may be invalid visually unpleasant errors will occur. In this paper, we exploit image nonlocal improve reproduction result. First, multiple directional...

The trade-off between spatial and spectral resolution is one of the fundamental issues in hyperspectral images (HSI). Given challenges directly acquiring high-resolution (HR-HSI), a compromised solution to fuse pair images: has (HR) domain but low-resolution (LR) spectral-domain other vice versa. Model-based image fusion methods including pan-sharpening aim at reconstructing HR-HSI by solving manually designed objective functions. However, such hand-crafted prior often leads inevitable...

The impressive conversational and programming abilities of ChatGPT make it an attractive tool for facilitating the education bioinformatics data analysis beginners. In this study, we proposed iterative model to fine-tune instructions guiding a chatbot in generating code tasks. We demonstrated feasibility by applying various topics. Additionally, discussed practical considerations limitations regarding use chatbot-aided education.

Abstract How the brain encodes, recognizes, and memorizes general visual objects is a fundamental question in neuroscience. Here, we investigated neural processes underlying object perception memory by recording from 3173 single neurons human amygdala hippocampus across four experiments. We employed both passive-viewing recognition tasks involving diverse range of naturalistic stimuli. Our findings reveal region-based feature code for objects, where exhibit receptive fields high-level space....

This paper presents a novel edge orientation adaptive interpolation scheme for resolution enhancement of still images. In order to achieve ideal adaptation, we propose estimate the local covariance characteristics at low but cleverly use them direct high based on invariant property orientation. The guarantees always go along not across it. Our new can generate images with dramatically higher visual quality than linear techniques while keeping computational complexity modest.

Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision. Searching spatially k-nearest neighbors a common strategy for extracting local information previous works. However, there no guarantee that the of correspondences are consistent because spatial distribution false often irregular. To address this issue, we present compatibility-specific mining method search neighbors. Moreover, order extract and aggregate more reliable features from neighbors,...

Deep learning has found successful applications in restoration of two-dimensional (2-D) images including denoising, dehazing, and superresolution. However, existing deep convolutional neural network (DCNN) architecture cannot fully exploit spatial-spectral correlations three-dimensional (3-D) hyperspectral (HSIs) (directly extending 2-D DCNN into 3-D will significantly increase computational complexity); meantime, unlike images, there is an obstacle caused by the shortage training data for...

Physical acquisition of high-resolution hyperspectral images (HR-HSI) has remained difficult, despite its potential resolving material-related ambiguities in vision applications. Deep image fusion, aiming at reconstructing an HR-HSI from a pair low-resolution (LR-HSI) and multispectral (HR-MSI), become appealing computational alternative. Existing fusion methods either rely on hand-crafted priors or treat as nonlinear mapping problem, ignoring important physical imaging models. In this...

Human action recognition has recently become one of the popular research topics in computer vision community. Various 3D-CNN based methods have been presented to tackle both spatial and temporal dimensions task video with competitive results. However, these suffered some fundamental limitations such as lack robustness generalization, e.g., how does ordering frames affect results? This work presents a novel end-to-end Transformer-based Directed Attention (Direc-Former) framework <sup...

Many deep learning-based solutions to blind image deblurring estimate the blur representation and reconstruct target from its blurry observation. However, these methods suffer severe performance degradation in real-world scenarios because they ignore important prior information about motion (e.g., is diverse spatially varying). Some have attempted explicitly non-uniform kernels by CNNs, but accurate estimation still challenging due lack of ground truth varying images. To address issues, we...

This paper introduces a new framework for error concealment in block-based image coding systems: sequential recovery. Unlike previous approaches that simultaneously recover the pixels inside missing block, we propose to them fashion such previously-recovered can be used recovery process afterwards. The principal advantage of approach is improved capability recovering important features brought by reduction complexity statistical modeling, i.e., from blockwise pixelwise. Under recovery,...

This paper explores the possibility of using multispectral iris information to enhance recognition performance an biometric system. Commercial systems typically sense iridal reflection pertaining near-infrared (IR) range electromagnetic spectrum. work examines represented in visible and IR portion It is hypothesized that, based on color eye, different components are highlighted at multiple wavelengths. To this end, acquisition procedure for obtaining co-registered images associated with IR,...

Blind image quality assessment refers to the problem of evaluating visual an without any reference. It addresses a fundamental distinction between fidelity and quality, i.e. human vision system usually does not need reference determine subjective target image. In this paper, we propose appraise by three objective measures: edge sharpness level, random noise level structural level. They jointly provide heuristic approach characterizing most important aspects quality. We investigate various...

Depth maps, characterizing per-pixel physical distance between objects in a 3D scene and capturing camera, can now be readily acquired using inexpensive active sensors such as Microsoft Kinect. However, the depth maps are often corrupted due to surface reflection or sensor noise. In this paper, we build on two previously developed works image denoising literature restore single maps-i.e., jointly exploit local smoothness nonlocal self-similarity of map. Specifically, propose first cluster...