Image Defogging Algorithm Based on Deblurgan Network

Abstract

The reduced visibility in foggy days impairs the quality of captured images and videos to varying degrees, leading to limited applications of these images in the field of computer vision. To solve this problem, direct recovery of fog-free images based on an improved Deblurgan network is proposed. We add the expanded convolution (Dialted Conv) module in the generator to expand the perceptual field to extract richer semantic information, and add the spatial attention mechanism module at specified locations to facilitate the elimination of residual fog; the discriminator uses the traditional PatchGAN for chunk determination to improve the discriminative accuracy; the loss function adds BCE loss to improve the discriminative accuracy of the discriminator and the pixel-level detail retention of the generator. In the synthetic fogged dataset RESIDE, the subjective visual comparison with dark channel, DehazeNet, AOD-Net, and defogging effect shows that the defogging effect of this network model and the detail information and color contrast of defogged images are improved; meanwhile, the objective evaluation indexes Peak Signalto Nise Rtioo a, PSNR and Structure Smilaritiy (SSIM) were also improved respectively.