Image dehazing is a challenging ill-posed problem in low-level computer vision tasks, requiring the restoration of high-quality, haze-free images from complex and foggy conditions. Deep learning-based dehazing methods struggle to effectively remove non-homogeneous fog distributions due to the uneven and dense nature of fog patches, making it difficult to clear real-world fog variations. A key challenge for non-homogeneous image dehazing algorithms is efficiently capturing the spatial distribution of haze in areas with varying fog densities while restoring fine image details. To address these challenges, we propose MLCANet, a multi-level composite attention-guided network for non-homogeneous image dehazing. MLCANet mitigates the impact of uneven haze areas through two main components: the Multi-level Composite Attention Generation Network (MCAGN) and the Dehazed Image Reconstruction Network (DIRN). The MCAGN integrates channel attention (CA), spatial attention (SA), and multi-scale pixel attention (MSPA) to capture haze features at different spatial scales. The DIRN, based on a decoder-encoder architecture, combines multi-scale dilated convolutions and deformable convolutions to restore fine image details more flexibly and efficiently. Extensive qualitative and quantitative experiments, along with ablation studies, demonstrate the effectiveness and feasibility of this method for non-homogeneous image dehazing.
Yongsheng Qiu (Fri,) studied this question.