Refined single image rain removal method based on generative adversarial network

A single image and network technology, applied in image enhancement, image analysis, image data processing, etc., can solve problems that cannot be applied in practical scenarios, and achieve the effect of improving image rain removal effect, increasing calculation amount, and eliminating rain marks residue

Active Publication Date: 2020-04-10
TIANJIN UNIV
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Problems solved by technology

However, although they have achieved good results on some data sets, due to the large changes in the dens

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  • Refined single image rain removal method based on generative adversarial network
  • Refined single image rain removal method based on generative adversarial network
  • Refined single image rain removal method based on generative adversarial network

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Embodiment Construction

[0058] The present invention will be further described in detail below in conjunction with the accompanying drawings and through specific embodiments. The following embodiments are only descriptive, not restrictive, and cannot limit the protection scope of the present invention.

[0059] A method for deraining a single image based on a generative confrontation network, the steps are as follows:

[0060] (1) Normalize the input image so that the value of the image is distributed between -1 and 1, which is convenient for the neural network to process.

[0061] The input of the present invention is an image with rain in RGB channels, if the input is an image with other channel numbers, the image needs to be converted into a 3-channel image. In addition, no additional processing is required to prevent loss of information in the original image. The data set used in the present invention is a synthetic data set for training. The synthetic dataset has a total of 12,000 training ima...

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Abstract

The invention relates to a refined single image rain removing method based on a generative adversarial network. Inputting the rain image into a rain streak estimation network; obtaining an estimated rain streak graph; connecting with an input image to form a multi-channel image; the rain-free image generated by the generation model is input into the discriminator for judgment, the generator is optimized according to a judgment result, a generator network with high rain removal capability is finally obtained, the output of the generation model serves as the input of the image refinement network, the image is further processed, and a final rain-free image is obtained. The algorithm provided by the invention is an end-to-end algorithm, and any additional preprocessing and post-processing arenot needed. Compared with other work of using a generative adversarial network to carry out a single image area, two auxiliary networks are provided, and the image rain removal effect can be further improved while the calculated amount is not significantly increased.

Description

technical field [0001] The invention belongs to the field of multimedia image processing, and relates to computer vision and deep learning technology, in particular to a method for removing rain from a single image based on a generative confrontation network. [0002] technical background [0003] Rain is the most common severe weather, and images obtained in rainy days often have poor visual effects. At the same time, many computer vision algorithms usually input clean images by default, so images with rain often have a bad impact on these algorithms and reduce their performance. The method of removing rain from a single image is to use the existing prior information to process the input image to obtain a clean image without rain. [0004] Deraining algorithms are mainly divided into video deraining and image deraining. Video and image deraining algorithms have been developed relatively maturely. These models can use the time information in the video to accurately locate r...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/001G06T2207/20081G06T2207/20084Y02A90/10
Inventor 侯永宏苏晓雨李士超郭子慧聂梦真
Owner TIANJIN UNIV
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