Image Snow Removal Methods for Robotic Environment Fusion

doi:10.3901/JME.2019.11.098

Abstract

Abstract: Aiming at the problem that snow weather affects the robustness of the fusion robotic vision system, a snow removal method based on snow model and deep learning is proposed. A simplified snow model is derived based on the snow imaging process, and a deep snow removal network is designed based on this model. The network consists of a snowflakes detection sub-network and a snowflakes removal sub-network. The snowflakes detection sub-network uses a residual learning network to accurately learn the difference between snow images and snow-free images. The desnowing sub-network adopts a densely connected U network. It usually can relieve the contradiction of over-desnowing and under-desnowing by using U-net to preserve image details and feature reuse of DenseNet to accuratelly remove snowflakes. Experiments show that the snow model-based deep networks can effectively detect and remove snowflakes from images.

Key words: deep learning, desnowing, environment fusion, robot

CLC Number:

TP242

LI Pengyue, TIAN Jiandong, WANG Guolin, LI Xiaomao, TANG Yandong, WU Chengdong. Image Snow Removal Methods for Robotic Environment Fusion[J]. Journal of Mechanical Engineering, 2019, 55(11): 98-104.

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