Single image rain removing method based on compressed reward and punishment neural network reusing original information

Abstract

The invention relates to a single image rain removing method based on a compressed reward and punishment neural network reusing original information. The single image rain removing method comprises the following steps: firstly, decomposing a rain image into a low-frequency image layer and a high-frequency image layer by utilizing rapid guide filtering; inputting the high-frequency image layer intoa neural network which combines a compressed reward and punishment neural network structure block, batch normalization processing and an original information connection reuse mode provided by the method to carry out feature learning and extraction, and removing rain lines in the neural network; and finally, adding the high-frequency layer without the rain line to the original low-frequency layerto obtain a final rain removal result. According to the single image rain removing method, rain removal is carried out on a single rain image, and compared with an existing traditional rain removal method and a rain removal method based on deep learning, a rain-free image with higher quality can be obtained; and in addition, the network proposed by the method is based on the compressed reward andpunishment neural network, and the compressed reward and punishment structure block used by the network proposed by the method can well describe the relationship between the feature channels, therebyimproving the expression ability of the network and improving the rain removal effect.

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 based on a compressed reward-punishment neural network that reuses original information. Background technique [0002] In rainy days, raindrop particles are generally larger than 100 μm and are easily captured by the lens. Rainy days will reduce the quality of the image and affect the color information in the image. Therefore, due to the influence of rainy weather conditions, some texture and detail information of images captured by outdoor lenses are easily blocked by rain lines, resulting in problems such as excessively bright local areas and blurred background images. The degradation of image quality in rainy days greatly restricts the functions of outdoor intelligent vision systems such as visual surveillance, visual navigation and target tracking. Moreover, the state of raindrop particles is changeable, and the directi...

AI Technical Summary

Problems solved by technology

Although these algorithms can achieve better results than traditional algorithms, some background details will still be lost in the rain removal results.

At the same time, some network layers are too deep, and the training cost is relatively high. For example, DetailNet contains 26 layers.

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