Single image deraining algorithm based on multi-scale dictionary

摘要

#$%^&*AU2020100460A420200430.pdf#####ABSTRACT We aim to remove the rain tracks from the rain images and retain the structure information of the original rain map to the greatest extent. Due to the complexity of the rain layer, the rainless background layer cannot be directly obtained at one time. Therefore, We according to the rain streaks of many aspects, such as sparsity, structural and directional information, proposed a new single image to the rain, which framework of the method through constant iterative update background layer, the sparse coefficient of the rain layer, the rain dictionary and a new rain layer, thereby gaining a free-rain image. Our main contribution can be divided into three parts: (I) A very effective convolutional sparse coding framework is proposed to iteratively update the rain layer and the background layer. (II) Considering the multi-scale characteristics of the noise rain layer information in the rain image taken in reality under different the depth of field, we proposed the method of learning multidictionary, and carried out the convolution sparse coding for the raindrop information of different sizes (III) In the process of solving the rain layer, we proposed to use the multi-scale dictionary to solve the updated rain layer information, and to use the consistency of rain direction and the structure of raindrops to propose two prior constraints based on gradient, so as to obtain better results. Finally, ADMM algorithm is used to solve the model alternately to obtain the rainless image with rich details.