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.

The application of optical remote sensing images (ORSIs) is prevalent in many fields. Accordingly, ORSI-oriented saliency 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 a 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 (BAM) and semantic-guided fusion modules (SGFM). Between these two, BAM uniformly assembles channel and spatial attention in an efficient and rational manner, addressing the open issue of dimension-wisely 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 RSIs-SOD methods proposed in recent years, with respect to accuracy, parameter size, computational cost, and FLOPS, as shown in Fig. 1. Code is available at https://github.com/ZhengJianwei2/BAFS-Net.

To cope with the challenges stemming from prolonged acquisition periods, compressed sensing MRI has emerged as a popular technique to accelerate the reconstruction of high quality images from under-sampled k-space data. Most current solutions endeavor to solve this issue with the pursuit of certain prior properties, yet the treatments are all enforced in the original space, resulting in limited feature information. To boost the performance yet with the guarantee of high running efficiency, in this study, we propose a Physics-Guided Implicit Unrolling Network (PGIUN). Specifically, by an elaborately designed reversible network, the inputs are first mapped to a channel-lifted implicit space, which taps the potential of capturing spatial-invariant features sufficiently. Within this implicit space, we then unfold an accelerated optimization algorithm to iterate an efficient and feasible solution, in which a parallelly dual-domain update is equipped for better feature fusion. Finally, an inverse embedding transformation of the recovered high-dimensional representation is employed to achieve the desired estimation. PGIUN enjoys high interpretability benefiting from the physically induced modules, which not only facilitates an intuitive understanding of the internal working mechanism but also endows it with high generalization ability. Extensive experiments conducted across diverse datasets and varying sampling rates/patterns consistently establish the superiority of our approach over state-of-the-art methods in both visual and quantitative evaluations.

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.

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 images (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, a global leading attention Module, and an expansion-integration module. First, FSCM uses dilated convolutions to enlarge the receptive field and furcate skip connections to capture more multi-scale 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, GLM locates the potentially significant object positions in the feature map from a global semantic perspective. Finally, through an attention-guided cascade structure, 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 MAE and 1.38% on F ^adp _β . Notably, the proposed CIF-Net runs with 40.40M parameters, 14.8 GFLOPs computational complexity, and 58 fps inference speed, which guarantees high efficiency.