Improved AODNet for Fast Image Dehazing

Application scenarios such as unmanned driving and UAV reconnaissance have the requirements of high performance, low delay and small space occupation. Images taken in foggy days are easy to be affected by fog or haze, thus losing some important information. The purpose of image dehazing is to remove the influence of fog on image quality, which is of great significance to assist in solving high-level vision tasks. Aiming at the shortcomings of the current defogging method, such as slow defogging speed and poor defogging effect, this paper introduces the idea of FPCNet and the attention mechanism module, and proposes an improved AODNet fast defogging algorithm to ensure the defogging speed and defogging performance. The public dataset RESIDE was used for training and testing. Experimental results show that in terms of dehazing performance, the proposed algorithm achieves 25.78 and 0.992 in PSNR and SSIM respectively. In terms of dehazing speed, the proposed method is close to AODNet, with only 5 times more parameters than AODNet, but more than 100 times smaller than other methods.