Vanilla convolution and window-based self-attention have shown significant success in image dehazing. However, they are constrained by limited receptive fields and ignore frequency gaps between dehazed and clear images. The former hampers the modeling of global dependencies, while the latter impedes the learning of high-frequency features, leading to suboptimal performance. In this paper, we propose the Joint Spatial and Fourier Convolutional Network (JSFC-Net), which leverages Fourier transformation to simultaneously address the two aforementioned problems with low computational overhead. We introduce the Frequency-Spatial Promoted and Physical Learning Block, which extracts high-level features from the spatial domain and frequency domain in parallel. We design a simple yet effective solution that uses spatial features to promote and modulate frequency features in a multi-scale manner, achieving refinement of frequency features and addressing robustness issue caused by global sensitivity. Additionally, we present the Receptive Field Selection Module to facilitate improved fusion of spatial and frequency domain features. Finally, we introduce frequency loss to further narrow frequency gaps. Comprehensive experiments on multiple datasets demonstrate that JSFC-Net is significantly superior to SOTA dehazing methods.

SAR-assisted thick cloud removal from optical remote sensing images has long been a challenging task. Current mainstream methods face challenges in achieving an effective global receptive field, fully utilizing multi-scale features, and deeply integrating features from both modalities. To overcome these limitations, we propose the Multi-scale Multi-branch Mamba model(M3-CR) for SAR-assisted thick cloud removal. Specifically, we integrate the Mamba model into the task of SAR-assisted cloud removal, effectively modeling global dependencies within the images. Concurrently, a Multi-scale Multi-branch structure is introduced to extract and integrate multi-scale information, and in combination with a convolutional branch to fully exploit the global and local geographic proximities inherent in remote sensing images. Furthermore, we present a novel feature fusion module leveraging the Modal-Traversing 2D Selective Scan(MTSS2D) to enable deep interaction and integration of features from optical and SAR images. The experimental results on two benchmark databases show that the M3-CR achieves superior performance compared to state-of-theart cloud removal approaches, while requiring fewer parameters and reduced FLOPs. The code for M3-CR will be made publicly available at https://github.com/LinpengPan/M3CR.

Camouflaged object detection (COD) is challenging for both human and computer vision, as targets often blend into the background by sharing similar color, texture, or shape. While many feature enhancement techniques exist, single-view methods tend to overemphasize certain Recognizing that camouflaged objects exhibit different concealment strategies under varying observational perspectives, we propose HUNTNet, a network that establishes a dynamic detection mechanism to decouple target features from RGB images and perform topological decamouflage across multiple homomorphic feature spaces through a unified feature focusing architecture. We adopt PVTv2 as the backbone to extract multi-perspective spatial features. Detail representation is enhanced via a feature module that integrates Dual-Channel Recursive (DCR), Wavelet-Gabor Transform (WGT), and Anisotropic Gradient Responding (AGR), which together improve boundary discrimination and edge contour detection. To further boost performance, the Simplicial Feature Integration (SFI) module recursively fuses multi-layer features, enabling high-resolution focus on target regions. Experiments show that HUNTNet surpasses state-of-the-art methods in both accuracy and generalization, offering a robust solution for COD and improving segmentation in complex scenes. Our code is available at https://github.com/HaolinJi817/HUNTNet.