Image rain removal method and system

Abstract

The invention discloses an image rain removal method and system. The method comprises the following steps: acquiring an image to be subjected to rain removal; inputting the image to be subjected to rain removal into the rain removal model to obtain an image subjected to rain removal, wherein the rain removal model is obtained by training a deep learning network by adopting training data, the deep learning network comprises a first convolutional layer, a long short-term memory module, a first residual block, a second residual block and a second convolutional layer which are connected in sequence; each of the first residual block and the second residual block comprises a third convolutional layer, a fourth convolutional layer and a linear unit function with leakage correction which are connected in sequence. The rain removal effect of the image can be improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method and system for removing rain from an image. Background technique [0002] As a common dynamic weather, rain often interferes with imaging systems. Due to the dynamics and randomness of raindrops, the accumulated rain marks will degrade the image quality and seriously affect the performance of outdoor vision systems, such as automatic driving, pedestrian detection, and target recognition. Therefore, image deraining is particularly important. [0003] The current methods for removing rain from a single image are mainly divided into two types: the first is a priori-based method, such as the bilateral filtering method that decomposes the rain image into low-frequency and high-frequency parts to separate rain marks; Show similar and repeated patterns in , build a deraining method for deraining low-rank representation model; separate the rain layer and non-rain layer from the ...

AI Technical Summary

Problems solved by technology

[0004] At present, the image deraining method based on deep learning uses a network with better deraining effect: a double recursive network (DUAL RECURSIVE NETWORK FOR FAST IMAGE DERAINING, DRN network) for fast image deraining, but the network exists as follows Disadvantages: (1) Although the feature map information can be extracted as much as possible through double recursion, the shallower network structure still has the problem of losing feature map information and extracting less feature information, and the rain removal effect needs to be improved; (2 ) The convolutional neural network algorithm adopted by the network, the activation function used in the hidden layer is the Rectified Linear Unit (ReLU) function, the ReLU function will cause the neurons in the neural network to die due to its own reasons, making the neural network The use efficiency of the nodes is low, which will reduce the available information and affect the deraining effect

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