Image rain removing method and device, readable storage medium and terminal equipment

Abstract

The invention relates to the technical field of image processing, in particular to an image rain removing method and device, a readable storage medium and terminal equipment. The method provided by the invention comprises the steps of obtaining an image with rain; inputting the rain-carrying image into the trained generative adversarial network model to obtain a clear rain-removed image of the rain-carrying image output by the generative adversarial network model; wherein the generative adversarial network model comprises a generative model and a discriminant model which are set as adversarialtraining; wherein the generation model is a full convolutional network model obtained by carrying out feature supervision training on the first image by utilizing features extracted from the second image; wherein the first image is a first training rain image; wherein the second image is a first training clear image corresponding to the first training rain image, so as to train the generative adversarial network model through feature supervision of the clear image, thereby optimizing the feature extraction function of the generative adversarial network model, improving the extraction accuracyof the original texture features of the image, and improving the rain removal effect of the image.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image rain removal method, device, computer-readable storage medium and terminal equipment. Background technique [0002] With the rapid development of image processing technology, target detection and recognition based on image analysis is widely used in various fields, such as automatic driving, target retrieval and other fields. Imaging blur and information coverage affect target detection and recognition, so it is necessary to remove rain from images with rain. [0003] At present, methods such as dictionary learning, non-local mean filtering, low-rank matrix decomposition, and Gaussian mixture model are usually used to remove rain. Remove the original texture information of the image, resulting in an over-smooth background, resulting in poor rain removal effect. Contents of the invention [0004] Embodiments of the present invention provide an image d...

AI Technical Summary

Problems solved by technology

[0002] With the rapid development of image processing technology, target detection and recognition based on image analysis is widely used in various fields, such as automatic driving, target retrieval and other fields. Imaging blur and information coverage affect target detection and recognition, so it is necessary to remove rain from images with rain

[0003] At present, methods such as dictionary learning, non-local mean filtering, low-rank matrix decomposition, and Gaussian mixture model are usually used to remove rain. Remove the original texture information of the image, resulting in an over-smooth background, resulting in poor rain removal effect

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