RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

Abstract

The advanced driving assistant system (ADAS) is an important vehicle safety technology that can effectively reduce traffic accidents. This system can perceive information about the surrounding environment through in-vehicle cameras. However, these cameras are easily affected by severe weather conditions, such as those involving fog, rain, and snow. The quality of the images acquired by the system is degraded, and the function of the ADAS is thus weakened. In response to this problem, we propose a comprehensive imaging model that can represent the features of fog, rain streaks, raindrops and snowflakes in an image. Subsequently, an algorithm called RASWNet is proposed, which can remove all severe weather features from a degraded image. Based on the generative adversarial network, RASWNet combines the focus capture ability of a visual attention mechanism, the memory ability of the recurrent neural network and the feature extraction ability of the dense blocks approach. We verify the network structure through several ablation studies and use various synthetic and real images to test it. The results of these experiments show that our algorithm is not only better than the commonly used algorithms in terms of its clarity enhancement capacity but is also suitable for all severe weather conditions. INDEX TERMS Generative adversarial network, remove all severe weather features, degraded image, RASWNet, visual attention mechanism.

Framework of degraded image restoration and simultaneous localization and mapping for multiple bad weather conditions

“…Similar studies to ours are Li et al's 56 and Gao et al's, 57 as they put the degradation restoration process caused by rain, haze, snow, and adjacent raindrops into a unified network. However, these works automatically switch the restoration pipelines depending on the bad weather and do not involve the mutual switching between degradation and nondegradation.…”

MSFSA-GAN: Multi-Scale Fusion Self Attention Generative Adversarial Network for Single Image Deraining

“…The output of the generator and the real rainless image are sent as the input of the discriminator, sent to the global pooling layer through the dense layer (DB), compared with the eigenvalues of the two images, and then output after FC and sigmoid [14]. So, the contributions of our paper are as follows:…”