Substation Inspection Image Dehazing Method Based on Decomposed Convolution and Adaptive Fusion

To combat the decline in substation image clarity resulting from adverse weather phenomena like haze, which often leads to poor illumination and altered color perception, a compact image dehazing model called the Substation Image Enhancement Network with Decomposition Convolution and Adaptive Fusion (SDCNet) is introduced. In contrast to traditional dehazing methods that expand the convolutional kernel to widen the receptive field and improve feature acquisition, commonly at the cost of increased parameters and computational load, SDCNet employs a decomposition-based convolutional enhancement module. This component efficiently extracts spatial features while keeping computation lightweight. Moreover, an adaptive fusion mechanism is incorporated to better align and merge features from both encoder and decoder stages, aiding in the retention of essential image information. To further enhance model learning, a contrastive regularization strategy is applied, leveraging both hazy and clear substation images during training. Empirical evaluations show that SDCNet substantially enhances visual brightness and restores accurate structural and color details. On the MIIS dataset of substation haze images, it delivers gains of 4.053 dB in PSNR and 0.006 in SSIM compared to current state-of-the-art approaches. Additional assessment on the SSDF dataset further confirms its reliability in detecting substation defects under unfavorable weather conditions.