Ensuring the structural continuity and completeness of road networks in high-resolution remote sensing imagery remains a major challenge for current deep learning methods, especially under conditions of occlusion caused by vegetation, buildings, or shadows. To address this, we propose a novel post-processing enhancement framework that improves the connectivity and accuracy of initial road extraction results produced by any segmentation model. The method employs a dual-stream encoder architecture, which jointly processes RGB images and preliminary road masks to obtain complementary spatial and semantic information. A core component is the MAF (Multi-Modal Attention Fusion) module, designed to capture fine-grained, long-range, and cross-scale dependencies between image and mask features. This fusion leads to the restoration of fragmented road segments, the suppression of noise, and overall improvement in road completeness. Experiments on benchmark datasets (DeepGlobe and Massachusetts) demonstrate substantial gains in precision, recall, F1-score, and mIoU, confirming the framework’s effectiveness and generalization ability in real-world scenarios.

Crack segmentation is crucial for health monitoring and preventive maintenance of concrete structures. However, the complex morphologies of cracks and the limited resources of mobile devices pose challenges for accurate and efficient segmentation. To address this, we propose an efficient dynamic-state-space-enhanced network termed DSS-MobileNetV3 for crack segmentation. The DSS-MobileNetV3 adopts a U-shaped encoder–decoder architecture, and a dynamic-state-space (DSS) block is designed into the encoder to improve the MobileNetV3 bottleneck module in modeling global dependencies. The DSS block improves the MobileNetV3 model in structural perception and global dependency modeling for complex crack morphologies by integrating dynamic snake convolution and a state space model. The decoder utilizes the upsampling and depthwise separable convolution to progressively decode and efficiently restore the spatial resolution. In addition, to suppress complex noise in the image background and highlight crack textures, the strip pooling module is introduced into the skip connection between the encoder and decoder for performance enhancement. Extensive experiments are conducted on three public crack datasets, and the proposed DSS-MobileNetV3 achieves SOTA performance in both accuracy and efficiency.

Recently, unsupervised deep learning based on the Retinex model for low‐light image enhancement (LLIE) has made significant progress. However, most existing methods typically assume gradual illumination changes and use single‐path network to first estimate illumination for image decomposition, then correct it for low‐light enhancement. This not only makes it difficult to effectively address low‐light noise and rapid illumination changes but also overlooks the potential amplification of original reflectance contrast, which limits their performance. To address these challenges, this work proposes an efficient algorithm called MDSC for LLIE, which comprises three functional modules: detail‐aware image smoothing (DAS), multipath image decomposition (MPD), and reflectance synergistic correction (RSC). Concretely, the input low‐light image is first smoothed with DAS to suppress noise while preserving details. Next, the smoothed image is processed by MPD to perform multipath directed decomposition and fusion, mitigating rapid variations in illumination and producing more accurate illumination and reflectance components. Finally, RSC jointly calibrates the illumination and reflectance components and reilluminates the scene to counteract potential amplification of reflectance contrast and produce the final enhanced result. Experimental results on challenging datasets demonstrate the effectiveness of the proposed MDSC, with both quantitative and qualitative outcomes significantly outperforming most state‐of‐the‐art approaches.