Single image rain removing method based on multi-scale aggregation features

Abstract

The invention belongs to the field of computer vision, and relates to a single image rain removing method based on multi-scale aggregation features. The method is based on a multi-scale feature aggregation dense connection convolutional network framework and is composed of encoding-decoding networks, and each encoding network corresponds to one decoding network; and the coding network records theposition of the maximum pooling index in the maximum pooling process through dimensionality reduction and down-sampling of the maximum pooling layer, and the pooling index guides the up-sampling recovery process of the corresponding decoding network. The coding network and the decoding network are the same in a convolution layer, are both feature aggregation densely connected with a convolution module, and only have the maximum pooling process different from the corresponding upsampling process. According to the method, the rain stripes with different densities can be effectively removed, andmeanwhile, the details of the image are well reserved.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to a method for removing rain from a single image based on multi-scale aggregation features. Background technique [0002] In rainy days, the impact of rain marks on images and videos is often undesirable, and rain marks will seriously affect the performance of many outdoor computer vision applications, such as surveillance systems and automatic driving systems. In the field of security and outdoor photography, these camera and camera equipment are easily affected by severe weather such as severe rainstorms when working outdoors. However, in general, the design of the camera system for collecting images outdoors in practical applications does not take into account the outdoor weather. influences. When encountering weather conditions such as heavy fog, heavy rain, and heavy snow outdoors, the visibility of people's sight and the clarity of camera equipment will be greatly affected. The p...

AI Technical Summary

Problems solved by technology

However, some of these algorithms have incomplete description of the characteristics of the rain line, resulting in unclean rain removal. The results of some algorithms lead to excessive smoothing of the image, and the restoration of the image background is not complete enough.

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