RABNet: A Residual Attention‐Based Network for Visibility Restoration in Hazy Images

Image dehazing enhances visibility and image quality, enabling better decision‐making in critical applications such as surveillance, remote sensing, and autonomous driving. However, single‐image dehazing remains challenging due to the need for smoothness in homogeneous regions, accurate edge preservation, texture fidelity, and artifact‐free reconstruction. In this paper, we propose RABNet, a novel deep learning framework for single‐image dehazing. Traditional approaches often suffer from lossy reconstruction and poor performance under uneven haze distribution. RABNet addresses these limitations by integrating three key modules: the Hazy Feature Extraction Module (HFEM), Visibility Restoration Module (VRM), and Dehazed Image Reconstruction Module (DIRM). The HFEM enhances the network’s ability to perceive haze characteristics across varying lighting, contrast, and structural scales, providing a strong foundation for restoration. The VRM promotes feature diversity by employing multiple activation functions and an attention mechanism to refine visibility cues. Finally, the DIRM reconstructs the clean image by estimating the residual haze component and combining it with the input. By modeling haze‐related features effectively, RABNet restores the underlying clean image while preserving color, texture, and structural details. Extensive experiments on both synthetic and real‐world datasets, including FRIDA, O‐HAZE, I‐HAZE, and NH‐HAZE, demonstrate the robustness and superior performance of RABNet, achieving state‐of‐the‐art results in terms of PSNR and SSIM.