Attention-fused single image rain removal method

Abstract

The invention relates to an attention-fused single image rain removal method, which comprises the following steps of: firstly, carrying out batch normalization processing on an input image, and then inputting the input image into an encoder network in which a three-dimensional attention mechanism and a residual dense block structure are combined to obtain high-dimensional features; the global feature relevance is calculated by using a Transform mechanism; reducing the feature matrix to the size of an original input image step by step by using a decoder constructed by a three-dimensional attention residual dense block to obtain an output image; in order to solve the problem that image structure information and detail high-frequency information are erased due to an algorithm in the rain removal process, multi-scale structural loss and a common rain removal loss function are combined to participate in rain removal network training. According to the invention, a rain-removed image with higher quality can be obtained by carrying out rain removal on a single rain image. According to the method, the three-dimensional attention mechanism, the Transform and the coder-decoder architecture are combined, so that the rain removal performance of the network can be better improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for removing rain from a single image fused with attention. Background technique [0002] In life and social environment application scenarios, there are mainly two types of image data: image data and video data. Video can be regarded as a sequence of images stacked continuously before and after one frame, and there is continuous data between adjacent frames of the original video. The early rain removal algorithm mainly processed video data at the initial stage, and the video rain removal algorithm can use the rich inter-frame information to help generate the rain removal image. The processing object of single image deraining algorithm is single image data. Unlike video data, single image loses a lot of inter-frame information between consecutive frames, so the research of single image deraining algorithm is more complicated. The research on the single image r...

AI Technical Summary

Problems solved by technology

Luo et al. proposed a single image rain removal algorithm based on dictionary learning. By learning strong mutual exclusion learning dictionary, using highly discriminable sparse coding to accurately separate the background layer and rain layer, this method can effectively separate the background and rain lines , but when the scene in the image is similar to the structure of the rain line, this method will regard the scene as the rain line, resulting in excessive smearing of the background

Li et al. used a Gaussian mixture model to separate the background layer and rain layer to adapt to multi-directional and different-scale rain patterns. This method requires the user to manually select a suitable area in the image to provide prior information to construct a Gaussian mixture model, which is inconvenient to handle. and long running time

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