Deep unfolding networks (DUNs) have recently emerged as a promising approach for hyperspectral image super-resolution (HSISR) by combining the benefits of nonlinear deep learning architectures with interpretable optimization techniques. Despite their advantages, current DUNs face significant challenges, particularly in approximating degradation matrices across both spatial and spectral dimensions, which results in complex and cumbersome model construction. By analyzing the difference between the upsampled low-resolution hyperspectral images (LRHS) and the true target image, we observed that the residual image exhibits strong sparsity, akin to noise. Leveraging this insight, we reformulate the HSISR problem as a robust principal component analysis (RPCA)-based denoising task, effectively eliminating the need for the complex approximation of spatial degradation matrix and its transpose. In addition, we introduce a Tensor Ring Transformer based on multilinear products as the prior term, wherein tokens are mapped to a tensor ring factor domain and the traditional dot product is replaced with a multilinear tensor ring product. This significantly reduces the computational complexity of the Transformer model, from \( \mathcal{O}(N^2d) \) to \( \mathcal{O}(Nr^2) \), with \( r

Image inpainting, a critical facet of computer vision, is in full bloom accompanied by the rapid innovation of convolution neural networks and transformers, revolutionizing the practical management of abnormity disposal, image editing, etc. Of these applications, face inpainting is more challenging due to the higher demand for semantic accuracy in key regions such as eyes and nose. Classical face inpainting methods are celebrated for their fast generation speed and refined texture details. However, they often lack the level of controllability required for complex tasks. In contrast, existing multi-modal controllable inpainting techniques offer enhanced guidance through image-text integration but tend to be time-consuming and produce suboptimal texture refinement. To address these limitations, we propose the Multi-modal Pseudo-style Embedded Transformer (MPET), a novel and efficient multi-modal inpainting algorithm that seamlessly integrates the strengths of both approaches, achieving state-of-the-art performance. Specifically, edge completion facilitates a cost-efficient and simple bridging of the contour continuity. Multi-modal pseudo-style generation amalgamates the image-text modalities, successfully embedding text features within the visual vectors, thereby culminating in the formation of pseudo-style diagrams rich in diverse attributes. On that basis, a controllable style-embedded siamese network is elaborated, effectively orchestrating the interaction among style attributes while ensuring high-precision pixel infusion. Extensive experiments on public datasets demonstrate the superiority of our approach through both quantitative and qualitative evaluations, highlighting its potential to advance the field of face inpainting.

In recent years, camouflage object detection has remained a significant challenge due to the high similarities between objects and backgrounds. Relying solely on convolutions with limited receptive fields or attentions with fixed ranges is in trouble with handling the size variability of cared objects. Moreover, camouflaged targets are frequently covered by their surroundings, with existing methods prone to erroneously identifying the occluded portions. To break the dilemma, we propose a multi-scale core-peripheral attention network (CPANet), mainly including two elaborations: core-peripheral mask attention (CPMA) and multi-scale weighted fusion (MSWF). CPMA boosts camouflaged features by employing core- and peripheral-based attention mechanisms, mitigating the influence of surrounding obstacles and enabling precise localization of concealed targets. Additionally, MSWF captures multi-scale low-level features to refine local details and manifest complete object representations. Extensive evaluations demonstrate that CPANet outperforms state-of-the-art methods across four widely used benchmarks.

Tensor Nuclear Norm (TNN) has been widely employed as a regularization term for hyperspectral image super-resolution (HSISR). However, conventional TNN constraints based on Discrete Fourier Transform (DFT) often suffer from rank estimation biases and an inability to effectively capture complex spectral-spatial correlations, limiting their efficacy in HSISR. To address these challenges, we propose a Nonlinear Learnable Triple-domain (NLT) transform framework that integrates nonlinear transform, DFT, and self-learning adaptation. This multi-stage process promotes singular value concentration, improving low-rank approximation and rank estimation accuracy. Building upon this framework, we develop an NL-transform-oriented tensor product, a truncated singular value decomposition (TSVD) operation, and a novel tensor nuclear norm (NLTN) tailored for HSISR. By incorporating spectral subspace estimation and clustering-based patch grouping, our approach effectively leverages spatial-spectral correlations and non-local self-similarities, leading to enhanced reconstruction quality. To further mitigate singular value over-penalization, we introduce a logarithmic-based generalized NLTNN (GNLTN) and formulate an optimization strategy based on the alternating direction method of multipliers (ADMM). Extensive experiments demonstrate that our method significantly outperforms existing approaches in terms of fusion accuracy and visual fidelity, setting new benchmarks for hyperspectral image super-resolution. The code is available at https://github.com/xuhonghui96/GNLTN.

The compressed sensing MRI aims to recover high-fidelity images from undersampled k-space data, which enables MRI acceleration and meanwhile mitigates problems caused by prolonged acquisition time, such as physiological motion artifacts, patient discomfort, and delayed medical care. In this regard, the deep unfolding network (DUN) has emerged as the predominant solution due to the benefits of better interpretability and model capacity. However, existing algorithms remain inadequate for two principal reasons. First, directly unrolling a typical optimization algorithm is ill-considered for the structure information and domain knowledge. Second, the incorporation of the MRI-oriented imaging mechanism is inadequate. To tackle these two issues, we propose a Memoryaugmented Dual-domain Unfolding Network (MDUNet). Particularly, the per-iteratively learned memory is held to facilitate a better efficacy of feature representation. Besides, with the scheme of memory augmentation alternatively employed in the k-space and image domain, both the regional structure and global information can be complementarily integrated in a spiral manner. Comprehensive experiments conducted on diverse datasets, sampling rates, and sampling patterns demonstrate that our method, while maintaining a relatively small number of parameters, surpasses the latest methods. Codes will be available on the GitHub homepage of the corresponding author.

Compared to the natural image community, infrared target detection suffers more challenges due to the severely tiny and low-contrast objects, especially in cases with obscuration from clutter and noise. The traditional solutions are susceptible to noise interference, which yields suboptimal performance lacking of contour and texture details. Meanwhile, due to the spatial invariance of convolutional layers, most deep learning-based methods locate small targets loosely during feature extraction, leading to serious omissions. To address these limitations, we propose a hierarchical microscale-aware network (HMNet) following an encoder-decoder structure that is mainly equipped with two novel modules: the holistic attention-aware (HAA) module and the scale-aware adaptive extraction (SAE) module. HAA integrates local and global cues via self-attention, depthwise separable convolutions, and dilated convolutions, which hammers at enhancing target features and ensuring accurate localization. As a complement, SAE employs multi-scale features and spatial-channel attention to acquire richer texture details while reducing background noise. The experiments on public datasets demonstrate that our method achieves state-of-the-art performance.

In recent studies, tensor singular value decomposition (TSVD) within the high-order (Ho) algebra has shed light on solving the Tensor Robust Principal Component Analysis (TRPCA) problem. However, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimal outcomes. To overcome this limitation, we propose a self-learning TSVD method that rectifies computational inefficiencies and learns a lossless transformation, inducing a lower average-rank tensor. This involves multiplying learnable semi-orthogonal matrices obtained through Tucker compression with the original tensor along all modes, resulting in a core tensor with enhanced inherent low rankness and new self-learning transform matrices. The semi-orthogonal transforms, acting as a crucial building block, enhance spatial low-rankness, facilitating the resolution of smaller-scale problems and the design of efficient algorithms. Additionally, a reweighting Schatten-p scheme is integrated into the self-learning HoTSVD to understand global low-rank correlations, offering an effective numerical solution. Finally, we develop an alternating direction method of multipliers (ADMM)-based algorithm as a solver. Experimental results on Light Field Images (LFI), showcase the superiority of our proposed method over previous state-of-the-art approaches.

With the prosperity of deep learning techniques, salient object detection in remote sensing images (RSI-SOD) is concomitantly in full flourishing. However, due to the inherent challenges such as uncertainty in object quantities and scales, cluttered backgrounds, and blurred edges arising from shadows, most current approaches struggle for salient feature learning with the aid of heavy model architecture, yet often result in barely satisfactory performance. Some methods compromise model complexity to improve efficiency, albeit with significantly degraded results. To earn a satisfactory balance of efficacy and efficiency, we propose a new network for RSI-SOD, namely SFANet, based on progressive semantic flow and uncertainty-aware refinement. Specifically, we design a global semantic enhancement block (GSEB) to reduce background interference and accurately localize salient objects of varying quantities and scales, which further consists of three modularized components, i.e., semantic extraction module (SEM), interscale fusion module (IFM), and deep semantic graph-inference module (DSGM). SEM together with IFM contributes to the effective aggregation of multi-scale contexts by extracting fused and progressive semantic cues. DSGM performs semantic inference to better localize salient objects with irregularities in scale and topological structure. Furthermore, we present an uncertainty-aware refinement module (URM) to recognize salient objects in cluttered backgrounds and effectively suppress shadows. Extensive experiments are conducted on three RSI-SOD datasets, from which superior results can be achieved by our SFANet, outperforming the other cutting-edge methods. The code is available at https://github.com/ZhengJianwei2/SFANet.

Due to the diversity of scales and shapes, the uncertainty of object position, and the complexity of edge details, the recent merging problem of salient object detection in optical remote sensing image (RSI-SOD) is a considerably challenging topic. To cope with the challenges, we propose a new cross-scale interaction network (CIFNet) equipped with the enlarged receptive field, which mainly contains three modules in an encoder–decoder architecture, including a furcate skip connection module (FSCM), a global leading attention module, and an expansion–integration module (EIM). First, the FSCM uses dilated convolutions to enlarge the receptive field and furcate skip connections to capture more multiscale contextual information, both of which facilitate the adaptability of the model to different sizes, shapes, and quantities of the target objects. Second, on the low-resolution branch, a global leading attention module (GLM) locates the potentially significant object positions in the feature map from a global semantic perspective. Finally, through an attention-guided cascade structure, the EIM seeks more delicate characteristics by refining the features in a coarse-to-fine fashion. Extensive experiments are conducted on two RSI-SOD datasets, from which superior results can be achieved by our CIFNet, outperforming the other state-of-the-art methods. Compared with the second-best method, the performance gain of our method reaches 3.45% on mean absolute error (MAE) and 1.38% on$F_{\beta} ^{\mathrm {adp}}$. Notably, the proposed CIF-Net runs with 40.40-M parameters, 14.8-GFLOPs computational complexity, and 58-frames/s inference speed, which guarantees high efficiency.

The application of optical remote sensing images (ORSIs) is prevalent in many fields. Accordingly, ORSI-oriented salient object detection (SOD) has attracted more attention in recent years. However, yet many previously proposed methods present appealing performance in natural scene images (NSIs), they are difficult to be directly extended to remote sensing images due to the more complex scenes, such as blended backgrounds and diversiform topological shapes. Most specifically designed models often fail to achieve satisfactory results due to the weak usage of edge information and the ignorance of attention loss. Besides, computational inefficiency often causes poor applicability. To solve these problems, we propose a new model, namely, Bidimensional Attention and Full-stage Semantic Guidance Network (BAFS-Net), containing an edge guidance branch and a mainstream detection branch. Concretely, edge guidance generates boundary information, in which supervision with border labels is imposed to highlight the salient regions and plays a complementary role on the main branch. The mainstream detection branch involves two important components, i.e., bidimensional attention modules (BAMs) and semantic-guided fusion modules (SGFMs). Between these two, BAM uniformly assembles channel and spatial attention in an efficient and rational manner, addressing the open issue of dimensionwisely attention computation. SGFM hammers at the fusion of high-level features and low-level features. Moreover, the semantic maps are employed to interact with SGFM in full stages. Our approach surpasses most state-of-the-art RSI-SOD methods proposed in recent years, with respect to the accuracy, parameter size, computational cost, and floating point operations per second (FLOPS). The code is available athttps://github.com/ZhengJianwei2/BAFS-Net.