关键词: 图像去雾, 图像融合, 伽玛校正, 非锐化掩膜, 暗通道先验

Abstract: To address the issues of halo artifacts, edge blurring, color distortion, and contrast imbalance commonly encountered in traditional physical model-based dehazing algorithms, this paper proposes a single-image dehazing algorithm integrating multi-scale processing and lightweight networks. Initially, this paper generates images with varying exposure levels to capture texture features across different illumination regions, enabling comprehensive scene characterization through fusion of dark channel information under multi-exposure conditions. Subsequently, wavelet transform is employed to integrate multi-scale dark channel prior transmission estimates with multi-scale gamma-corrected image transmission estimates, which are further optimized using unsharp mask-guided filtering to enhance edge preservation capabilities. An adaptive regularization constraint mechanism is introduced to dynamically adjust smoothing intensity, effectively balancing detail retention and noise suppression. Finally, multi-scale luminance guidance combined with a lightweight network is adopted for atmospheric light estimation, enabling haze-free image reconstruction through inverse atmospheric scattering model computation. Extensive quantitative and qualitative experiments conducted on SOTS, RTTS, and HSTS datasets demonstrate that compared with conventional dehazing algorithms, the proposed method exhibits superior performance in both objective metrics and subjective visual quality, particularly showing enhanced restoration effectiveness in regions with abrupt depth variations.

Key words: image dehazing, image fusion, gamma correction, unsharp mask, dark channel prior