Adaptive Convolutional Strategy for Robust Image Dehazing in Diverse Environments

Adverse weather conditions such as haze, fog, and smog degrade image visibility, adversely affecting the performance of vision-based systems. Existing dehazing methods often struggle with non-uniform haze distributions, limited detail restoration, and poor generalization across diverse scenes. To overcome these limitations, this paper presents a deep learning-based dehazing framework that jointly restores image clarity and detail. Unlike conventional algorithms that often neglect fine structure recovery, our architecture incorporates four specialized sub-modules: (i) a noise attention module for enhancing noise suppression and feature preservation; (ii) an adaptive ConvNet module; (iii) a feature extraction module for capturing salient image features; and (iv) a detail refinement module to enhance spatial fidelity. The architecture is trained in an end-to-end manner to restore both structural integrity and colour consistency under challenging conditions. Extensive experiments conducted on synthetic and real-world datasets, including indoor, outdoor, underwater, night-time, and remote sensing scenarios, demonstrate superior generalization capability. In the SOTS indoor dataset, our method achieves a PSNR of 28.44 dB and an SSIM of 0.967, outperforming several state-of-the-art methods. Evaluations using additional metrics such as CIEDE2000 and MSE confirm the effectiveness of the proposed method in handling dense and heterogeneous haze while preserving fine textures and visual fidelity.