Scale Recurrent Network for Single Image Dehazing
Wataru Imai, Yosuke Ueki, Masaaki Ikehara · 2022
The quality of the images taken outside is directly affected by floating atmospheric particles. To keep the quality of the image, haze removal methods play a critical role. In this paper, we propose a new multi-scale network structure that is rarely used in haze removal and a new loss function to generate high-quality haze-free images. Despite a number of proposed dehazing methods, they are not able to capture the haze accurately without being confused by other objects that are similar in color to haze and completely remove the haze throughout the image. Our proposed network architecture takes the Scale Recurrent Network structure and we incorporate dilated convolution to catch the more global features like haze with the wide receptive field. Additionally, we train our network with a new loss function using dark channel prior to more effectively learn the haze features. Compared with state-of-the-art methods, our proposed method achieves better results on both synthetic and real-world images.