Adaptive defogging method for transmission line inspection images based on multilayer perceptual fusion

Abstract

Existing image defogging methods generally have problems such as incomplete defogging and color distortion. To address this problem, this paper proposes an adaptive defogging method for transmission line images based on multilayer perceptual fusion, which uses dynamic convolution, dense residuals, and attention mechanism to design an adaptive feature enhancement network containing six Dy-namic Residual Components (DRC) and two Dy-namic Skip-Connected Feature Fusion Component (DSCFF) composed of adaptive feature enhancement network, which prevents the problem of features being forgotten in the early stage of the network, and enhances the expressive ability of the model. For the decoding network, the de-fogging effect of the model is further strengthened by introducing a decoder module based on the SOS enhancement model, and finally, by comparing the experiments with the current de-fogging methods with more advanced performance, the results show that the method has good de-fogging effect and can retain the image details better with high color retention.