Anthropogenic effects on soils in the eastern Tibetan Plateau revealed by geochemical elemental characteristics.

The Tibetan Plateau (TP) constitutes a fragile and sensitive ecological environment, which is vulnerable to global climate change and human activities. To investigate the anthropogenic effects on the TP's environmental system is valuable for guiding human responses and adaptations to future environmental changes. In this study, we detailedly analyzed the geochemical elements of four representative soil sections developed on loess from Ganzi, Jinchuan, Aba, and Chuanzhusi in the eastern TP. The chemical elemental profiles distinctly indicated the presence of typical anthropogenic elements (Cu, Zn, Ni, Cr, Pb, Mn, and Fe), underscoring the substantial influence of human activities on TP soil, and showing spatial variance. Our results indicate that anthropogenic impacts were relatively low at Aba and Ganzi, resulting in a deficit of anthropogenic elements at the surface layer. Whereas at Jinchuan and Chuanzhusi, relatively intense anthropogenic impacts have led to the enrichment of anthropogenic elements in the topsoil. We infer that agricultural activities, increased traffic, and expansion of tourism activities were the major factors affecting the anthropogenic elements of TP soils. Our study highlights the impact of human activities on soil geochemical processes in the Tibetan Plateau.

Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks.

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)-based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.

Multiple Complementary Priors for Multispectral Image Compressive Sensing Reconstruction.

Compressive sensing (CS) techniques using a few compressed measurements have drawn considerable interest in reconstructing multispectral imagery (MSI). Nonlocal-based tensor methods have been widely used for MSI-CS reconstruction, which employ the nonlocal self-similarity (NSS) property of MSI to obtain satisfactory results. However, such methods only consider the internal priors of MSI while ignoring important external image information, for example deep-driven priors learned from a corpus of natural image datasets. Meanwhile, they usually suffer from annoying ringing artifacts due to the aggregation of overlapping patches. In this article, we propose a novel approach for highly effective MSI-CS reconstruction using multiple complementary priors (MCPs). The proposed MCP jointly exploits nonlocal low-rank and deep image priors under a hybrid plug-and-play framework, which contains multiple pairs of complementary priors, namely, internal and external, shallow and deep, and NSS and local spatial priors. To make the optimization tractable, a well-known alternating direction method of multiplier (ADMM) algorithm based on the alternating minimization framework is developed to solve the proposed MCP-based MSI-CS reconstruction problem. Extensive experimental results demonstrate that the proposed MCP algorithm outperforms many state-of-the-art CS techniques in MSI reconstruction. The source code of the proposed MCP-based MSI-CS reconstruction algorithm is available at: https://github.com/zhazhiyuan/MCP_MSI_CS_Demo.git.

Cross-Sectional and Longitudinal Associations of Comorbidities with Knee Symptoms and Radiographic Abnormalities of Osteoarthritis.

INTRODUCTION: This study aimed to investigate the associations of comorbidities with knee symptoms and radiographic abnormalities of osteoarthritis (OA). METHODS: Participants were from the Osteoarthritis Initiative. Comorbidities were identified at baseline using the modified Charlson Comorbidity Index. For both knees, symptoms were assessed annually from baseline to 48 months using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function scores (rescaled range 0-100), and radiographic abnormalities using the Kellgren-Lawrence (KL, 0-4) grades. The presence of significant pain and functional disability was defined as a WOMAC score of ≥ 25 and ≥ 22, respectively, and radiographic OA (ROA) as KL ≥ 2. An increase of ≥ 9 in WOMAC scores and ≥ 1 in KL grades were defined as symptomatic and radiographic progression, respectively. RESULTS: Of 3337 participants, 28% and 9% had one and ≥ 2 comorbidities, respectively. The number of comorbidities was associated with the presence of significant functional disability (odds ratios [ORs] 1.15; 1.46) and predicted the progression of both knee pain and functional disability (ORs 1.11; 1.51). For the type of comorbidities, non-OA musculoskeletal diseases were associated with the presence of ROA and significant functional disability (ORs 1.63; 1.82) and showed a trend to predict incident ROA (OR 1.84, 95% confidence interval 1.00-3.38 p = 0.051). Diabetes and kidney diseases were associated with symptomatic progression of OA (ORs 1.38; 2.72). CONCLUSIONS: Having more comorbidities, especially diabetes and kidney diseases, is associated with symptomatic progression of knee OA. Moreover, non-OA musculoskeletal diseases may be associated with the presence and onset of ROA.

STGlow: A Flow-Based Generative Framework With Dual-Graphormer for Pedestrian Trajectory Prediction.

The pedestrian trajectory prediction task is an essential component of intelligent systems. Its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, accurately forecasting their future trajectory is challenging. Existing approaches commonly adopt generative adversarial networks (GANs) or conditional variational autoencoders (CVAEs) to generate diverse trajectories. However, GAN-based methods do not directly model data in a latent space, which may make them fail to have full support over the underlying data distribution. CVAE-based methods optimize a lower bound on the log-likelihood of observations, which may cause the learned distribution to deviate from the underlying distribution. The above limitations make existing approaches often generate highly biased or inaccurate trajectories. In this article, we propose a novel generative flow-based framework with a dual-graphormer for pedestrian trajectory prediction (STGlow). Different from previous approaches, our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Besides, our method has clear physical meanings for simulating the evolution of human motion behaviors. The forward process of the flow gradually degrades complex motion behavior into simple behavior, while its reverse process represents the evolution of simple behavior into complex motion behavior. Furthermore, we introduce a dual-graphormer combined with the graph structure to more adequately model the temporal dependencies and the mutual spatial interactions. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.

Development and clinical applications of an enclosed automated targeted NGS library preparation system.

The rapid development of next-generation sequencing (NGS) technology has promoted its wide clinical application in precision medicine for oncology. However, laborious and time-consuming manual operations, highly skilled personnel requirements, and cross-contamination are major challenges for the clinical implementation of NGS technology-based tests. The Automated NGS Diagnostic Solutions (ANDiS) 500 system is a fully enclosed cassette-dependent automated NGS library preparation system. This platform could produce qualified targeted amplicon library in three steps with only 15 min of hands-on time. Rigorous cross-contamination test using simulated contaminant plasmids confirmed that the design of disposable cassette guarantees zero sample cross-contamination. The BRCA1 and BRCA2 mutation detection panel and gastrointestinal cancer-related gene analysis panel for the ANDiS 500 platform showed 100% accuracy and precision in detecting germ-line mutations and somatic mutations respectively. Furthermore, those panels showed 100% concordance with verified methods in a prospective cohort study enrolling 363 patients and a cohort of 45 pan-cancer samples. In conclusion, the ANDiS 500 automated platform could overcome major challenges for implementing NGS assays clinically and is eligible for routine clinical tests.

Development of Regular Hydrophobic Silica Aerogel Microspheres for Efficient Oil Adsorption.

The objective of this research was to develop new hydrophobic silica aerogel microspheres (HSAMs) with water glass and hexmethyldisilazane for oil adsorption. The effects of the hexmethyldisilazane concentration and drying method on the structure and organic liquid adsorption capacity were investigated. The hexmethyldisilazane concentration of the modification solution did not influence the microstructure and pore structure in a noteworthy manner, which depended more on the drying method. Vacuum drying led to more volume shrinkage of the silica gel microsphere (SGM) than supercritical CO2 drying, thus resulting in a larger apparent density, lower pore volume, narrower pore size distribution, and more compact network. Owing to the large pore volume and pore size, the HSAMs synthesized via supercritical CO2 drying had a larger organic liquid adsorption capacity. The adsorption capacities of the HSAMs with pore volumes of 4.04-6.44 cm3/g for colza oil, vacuum pump oil, and hexane are up to 18.3, 18.9, and 11.8 g/g, respectively, higher than for their state-of-the-art counterparts. The new sorbent preparation method is facile, cost-effective, safe, and ecofriendly, and the resulting HSAMs are exceptional in capacity, stability, and regenerability.

Low-Rankness Guided Group Sparse Representation for Image Restoration.

As a spotlighted nonlocal image representation model, group sparse representation (GSR) has demonstrated a great potential in diverse image restoration tasks. Most of the existing GSR-based image restoration approaches exploit the nonlocal self-similarity (NSS) prior by clustering similar patches into groups and imposing sparsity to each group coefficient, which can effectively preserve image texture information. However, these methods have imposed only plain sparsity over each individual patch of the group, while neglecting other beneficial image properties, e.g., low-rankness (LR), leads to degraded image restoration results. In this article, we propose a novel low-rankness guided group sparse representation (LGSR) model for highly effective image restoration applications. The proposed LGSR jointly utilizes the sparsity and LR priors of each group of similar patches under a unified framework. The two priors serve as the complementary priors in LGSR for effectively preserving the texture and structure information of natural images. Moreover, we apply an alternating minimization algorithm with an adaptively adjusted parameter scheme to solve the proposed LGSR-based image restoration problem. Extensive experiments are conducted to demonstrate that the proposed LGSR achieves superior results compared with many popular or state-of-the-art algorithms in various image restoration tasks, including denoising, inpainting, and compressive sensing (CS).

A Principled Design of Image Representation: Towards Forensic Tasks.

Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on the proper image representation. Despite the importance, current theoretical understanding for such representation remains limited, with varying degrees of neglect for its key role. For this gap, we attempt to investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. Our work starts from the abstraction of basic principles that the representation for forensics should satisfy, especially revealing the criticality of robustness, interpretability, and coverage. At the theoretical level, we propose a new representation framework for forensics, called dense invariant representation (DIR), which is characterized by stable description with mathematical guarantees. At the implementation level, the discrete calculation problems of DIR are discussed, and the corresponding accurate and fast solutions are designed with generic nature and constant complexity. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors. Also, at the application level, the proposed DIR is initially explored in passive and active forensics, namely copy-move forgery detection and perceptual hashing, exhibiting the benefits in fulfilling the requirements of such forensic tasks.

Deep Posterior Distribution-Based Embedding for Hyperspectral Image Super-Resolution.

In this paper, we investigate the problem of hyperspectral (HS) image spatial super-resolution via deep learning. Particularly, we focus on how to embed the high-dimensional spatial-spectral information of HS images efficiently and effectively. Specifically, in contrast to existing methods adopting empirically-designed network modules, we formulate HS embedding as an approximation of the posterior distribution of a set of carefully-defined HS embedding events, including layer-wise spatial-spectral feature extraction and network-level feature aggregation. Then, we incorporate the proposed feature embedding scheme into a source-consistent super-resolution framework that is physically-interpretable, producing PDE-Net, in which high-resolution (HR) HS images are iteratively refined from the residuals between input low-resolution (LR) HS images and pseudo-LR-HS images degenerated from reconstructed HR-HS images via probability-inspired HS embedding. Extensive experiments over three common benchmark datasets demonstrate that PDE-Net achieves superior performance over state-of-the-art methods. Besides, the probabilistic characteristic of this kind of networks can provide the epistemic uncertainty of the network outputs, which may bring additional benefits when used for other HS image-based applications. The code will be publicly available at https://github.com/jinnh/PDE-Net.

SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising.

Deep learning (DL) based hyperspectral images (HSIs) denoising approaches directly learn the nonlinear mapping between noisy and clean HSI pairs. They usually do not consider the physical characteristics of HSIs. This drawback makes the models lack interpretability that is key to understanding their denoising mechanism and limits their denoising ability. In this paper, we introduce a novel model-guided interpretable network for HSI denoising to tackle this problem. Fully considering the spatial redundancy, spectral low-rankness, and spectral-spatial correlations of HSIs, we first establish a subspace-based multidimensional sparse (SMDS) model under the umbrella of tensor notation. After that, the model is unfolded into an end-to-end network named SMDS-Net, whose fundamental modules are seamlessly connected with the denoising procedure and optimization of the SMDS model. This makes SMDS-Net convey clear physical meanings, i.e., learning the low-rankness and sparsity of HSIs. Finally, all key variables are obtained by discriminative training. Extensive experiments and comprehensive analysis on synthetic and real-world HSIs confirm the strong denoising ability, strong learning capability, promising generalization ability, and high interpretability of SMDS-Net against the state-of-the-art HSI denoising methods. The source code and data of this article will be made publicly available at https://github.com/bearshng/smds-net for reproducible research.

Weighted Error Entropy-Based Information Theoretic Learning for Robust Subspace Representation.

In most of the existing representation learning frameworks, the noise contaminating the data points is often assumed to be independent and identically distributed (i.i.d.), where the Gaussian distribution is often imposed. This assumption, though greatly simplifies the resulting representation problems, may not hold in many practical scenarios. For example, the noise in face representation is usually attributable to local variation, random occlusion, and unconstrained illumination, which is essentially structural, and hence, does not satisfy the i.i.d. property or the Gaussianity. In this article, we devise a generic noise model, referred to as independent and piecewise identically distributed (i.p.i.d.) model for robust presentation learning, where the statistical behavior of the underlying noise is characterized using a union of distributions. We demonstrate that our proposed i.p.i.d. model can better describe the complex noise encountered in practical scenarios and accommodate the traditional i.i.d. one as a special case. Assisted by the proposed noise model, we then develop a new information-theoretic learning framework for robust subspace representation through a novel minimum weighted error entropy criterion. Thanks to the superior modeling capability of the i.p.i.d. model, our proposed learning method achieves superior robustness against various types of noise. When applying our scheme to the subspace clustering and image recognition problems, we observe significant performance gains over the existing approaches.

A Hybrid Structural Sparsification Error Model for Image Restoration.

Recent works on structural sparse representation (SSR), which exploit image nonlocal self-similarity (NSS) prior by grouping similar patches for processing, have demonstrated promising performance in various image restoration applications. However, conventional SSR-based image restoration methods directly fit the dictionaries or transforms to the internal (corrupted) image data. The trained internal models inevitably suffer from overfitting to data corruption, thus generating the degraded restoration results. In this article, we propose a novel hybrid structural sparsification error (HSSE) model for image restoration, which jointly exploits image NSS prior using both the internal and external image data that provide complementary information. Furthermore, we propose a general image restoration scheme based on the HSSE model, and an alternating minimization algorithm for a range of image restoration applications, including image inpainting, image compressive sensing and image deblocking. Extensive experiments are conducted to demonstrate that the proposed HSSE-based scheme outperforms many popular or state-of-the-art image restoration methods in terms of both objective metrics and visual perception.

Optimal Pre-Filtering for Improving Facebook Shared Images.

Online Social Networks (OSNs) have attracted a huge number of users, who store and share various images on a daily basis. As a well-known fact, most OSN platforms apply a series of lossy operations on the uploaded images, which could severely degrade the quality of the shared images, negatively affecting the user experiences. In this work, we consider the problem of significantly improving OSN-shared images through applying an optimal pre-filtering prior to image sharing, without any cooperation from the OSN platform itself. Facebook, as one of the most popular and representative OSNs, is chosen as the platform to present our designed pre-filtering strategy. We first treat Facebook as a black box, and thoroughly recover its mechanism of processing color images. Based on the precise knowledge on the image processing pipeline on Facebook, we design the pre-filter under an optimization framework, minimizing the end-to-end distortion between the shared image and the original one. Compared with the directly shared images, our proposed pre-filtering-then-sharing strategy brings significant improvements in terms of both quantitative and qualitative metrics. Extensive experimental results are provided to show the superiority of our proposed method. Finally, we discuss the strategy on how to extend our proposed technique to other OSN platforms.

Triply Complementary Priors for Image Restoration.

Recent works that utilized deep models have achieved superior results in various image restoration (IR) applications. Such approach is typically supervised, which requires a corpus of training images with distributions similar to the images to be recovered. On the other hand, the shallow methods, which are usually unsupervised remain promising performance in many inverse problems, e.g., image deblurring and image compressive sensing (CS), as they can effectively leverage nonlocal self-similarity priors of natural images. However, most of such methods are patch-based leading to the restored images with various artifacts due to naive patch aggregation in addition to the slow speed. Using either approach alone usually limits performance and generalizability in IR tasks. In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely, internal and external, shallow and deep, and non-local and local priors. We then propose a novel hybrid plug-and-play (H-PnP) framework based on the LRD model for IR. Following this, a simple yet effective algorithm is developed to solve the proposed H-PnP based IR problems. Extensive experimental results on several representative IR tasks, including image deblurring, image CS and image deblocking, demonstrate that the proposed H-PnP algorithm achieves favorable performance compared to many popular or state-of-the-art IR methods in terms of both objective and visual perception.

Establishment of a modified percutaneous CT-guided paraspinal intramuscular VX-2 squamous cell carcinoma dual tumor model in rabbits.

BACKGROUND: The rabbit VX-2 tumor model is a commonly used transplanted tumor model and is widely used in surgical, radiological, and interventional studies. Most of the known tumor models for each site are single solid tumors. This study aimed to establish an accurate and stable intramuscular dual tumor model guided by computed tomography (CT). METHODS: In this study, we compared three different inoculation methods to select the most appropriate dual tumor model. Six New Zealand White rabbits were used as tumor-carrying rabbits for tumor harvesting. Thirty rabbits were divided into three groups as experimental rabbits. Group A applied the tumor cell suspension method, in which the suspension was injected into the designated location with a syringe under CT guidance. Groups B and C used tumor tissue strips obtained in vivo or under direct in vitro vision. The tumor tissue strips were implanted into the designated locations using a guide needle under CT guidance. The differences in tumorigenic rate, the size difference between bilateral tumors, and metastasis between the three methods were compared. RESULTS: It was found that group A obtained a 100% tumor survival rate, but the size of the tumor was more variable, and needle tract implantation metastasis occurred in 5 cases. In group B, tumor tissue strips were taken in vivo for implantation, in which one case failed to survive. Tumor tissue strips in group C were obtained in vitro under direct vision. The tumor tissue strips obtained in vitro by puncture using a biopsy needle in group C had a 100% tumorigenicity rate and stable tumor size. No significant needle tract implantation metastases were found in either group B or C. The variance of tumor size obtained in group A was significantly higher than in groups B and C. The variance of tumor size in group C was the smallest. Group C had high tumorigenicity and a more stable size and morphology of the formed tumors. CONCLUSION: The results showed that the method of obtaining tumor tissue strips using in vitro direct vision puncture and implanting them into the muscle with CT guidance and guide needles can establish an accurate and stable dual tumor model. This dual tumor model can provide substantial support for relevant preclinical studies.

Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models.

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, e.g., JS enforces the sparse codes to share the same support, or too general, e.g., GSC imposes only plain sparsity on the group coefficients, which limit their effectiveness for modeling real images. In this paper, we propose a novel NSS-based sparsity model, namely, low-rank regularized group sparse coding (LR-GSC), to bridge the gap between the popular GSC and JS. The proposed LR-GSC model simultaneously exploits the sparsity and low-rankness of the dictionary-domain coefficients for each group of similar patches. An alternating minimization with an adaptive adjusted parameter strategy is developed to solve the proposed optimization problem for different image restoration tasks, including image denoising, image deblocking, image inpainting, and image compressive sensing. Extensive experimental results demonstrate that the proposed LR-GSC algorithm outperforms many popular or state-of-the-art methods in terms of objective and perceptual metrics.

In-Situ Laser Polishing Additive Manufactured AlSi10Mg: Effect of Laser Polishing Strategy on Surface Morphology, Roughness and Microhardness.

Laser polishing is a widely used technology to improve the surface quality of the products. However, the investigation on the physical mechanism is still lacking. In this paper, the established numerical transient model reveals the rough surface evolution mechanism during laser polishing. Mass transfer driven by Marangoni force, surface tension and gravity appears in the laser-induced molten pool so that the polished surface topography tends to be smoother. The AlSi10Mg samples fabricated by laser-based powder bed fusion were polished at different laser hatching spaces, passes and directions to gain insight into the variation of the surface morphologies, roughness and microhardness in this paper. The experimental results show that after laser polishing, the surface roughness of Ra and Sa of the upper surface can be reduced from 12.5 µm to 3.7 µm and from to 29.3 µm to 8.4 µm, respectively, due to sufficient wetting in the molten pool. The microhardness of the upper surface can be elevated from 112.3 HV to 176.9 HV under the combined influence of the grain refinement, elements distribution change and surface defects elimination. Better surface quality can be gained by decreasing the hatching space, increasing polishing pass or choosing apposite laser direction.

Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual Learning.

This paper explores the problem of hyperspectral image (HSI) super-resolution that merges a low resolution HSI (LR-HSI) and a high resolution multispectral image (HR-MSI). The cross-modality distribution of the spatial and spectral information makes the problem challenging. Inspired by the classic wavelet decomposition-based image fusion, we propose a novel lightweight deep neural network-based framework, namely progressive zero-centric residual network (PZRes-Net), to address this problem efficiently and effectively. Specifically, PZRes-Net learns a high resolution and zero-centric residual image, which contains high-frequency spatial details of the scene across all spectral bands, from both inputs in a progressive fashion along the spectral dimension. And the resulting residual image is then superimposed onto the up-sampled LR-HSI in a mean-value invariant manner, leading to a coarse HR-HSI, which is further refined by exploring the coherence across all spectral bands simultaneously. To learn the residual image efficiently and effectively, we employ spectral-spatial separable convolution with dense connections. In addition, we propose zero-mean normalization implemented on the feature maps of each layer to realize the zero-mean characteristic of the residual image. Extensive experiments over both real and synthetic benchmark datasets demonstrate that our PZRes-Net outperforms state-of-the-art methods to a significant extent in terms of both 4 quantitative metrics and visual quality, e.g., our PZRes-Net improves the PSNR more than 3dB, while saving 2.3× parameters and consuming 15× less FLOPs. The code is publicly available at https://github.com/zbzhzhy/PZRes-Net.

Group Sparsity Residual Constraint with Non-Local Priors for Image Restoration.

Group sparse representation (GSR) has made great strides in image restoration producing superior performance, realized through employing a powerful mechanism to integrate the local sparsity and nonlocal self-similarity of images. However, due to some form of degradation (e.g., noise, down-sampling or pixels missing), traditional GSR models may fail to faithfully estimate sparsity of each group in an image, thus resulting in a distorted reconstruction of the original image. This motivates us to design a simple yet effective model that aims to address the above mentioned problem. Specifically, we propose group sparsity residual constraint with nonlocal priors (GSRC-NLP) for image restoration. Through introducing the group sparsity residual constraint, the problem of image restoration is further defined and simplified through attempts at reducing the group sparsity residual. Towards this end, we first obtain a good estimation of the group sparse coefficient of each original image group by exploiting the image nonlocal self-similarity (NSS) prior along with self-supervised learning scheme, and then the group sparse coefficient of the corresponding degraded image group is enforced to approximate the estimation. To make the proposed scheme tractable and robust, two algorithms, i.e., iterative shrinkage/thresholding (IST) and alternating direction method of multipliers (ADMM), are employed to solve the proposed optimization problems for different image restoration tasks. Experimental results on image denoising, image inpainting and image compressive sensing (CS) recovery, demonstrate that the proposed GSRC-NLP based image restoration algorithm is comparable to state-of-the-art denoising methods and outperforms several state-of-the-art image inpainting and image CS recovery methods in terms of both objective and perceptual quality metrics.