Deep Multi-scale Residual Learning for Single Image De-Hazing

Abstract

Images play an important role in various applications such as computer vision, scenario analysis, object detection, advanced driver assistance system (ADAS) etc. Usually images that are captured from outdoor get degraded due to the presence of turbid media and causes the outdoor vision system to provide deficient performance. The turbid media can be rain, haze, smog, fog, snow etc. With the help of developing technologies, it has become easier for everyone to recover such kind of distorted images. In order to overcome the limitations of existing models a new variant called deep multi-scale residual network has been proposed. The multi-scale deep residual learning network along with the simplified convolutional neural network map the hazy image to its corresponding haze-free image there by providing dehazed output image. The basic building block of the multi-scale residual network, passes the output from the previous layer and is fed to next layer. So that the possible color distortion in the recovered image would be avoided. The simplified CNN designed with the help of generic agnostic model helps in enhancing the haze-free image. Qualitative and quantitative analysis is performed. A simplified architecture with less parameters is designed and the model shows an excellent performance compared to state-of-art methods.