Underwater image denoising method based on generative adversarial network

An underwater image and network technology, applied in biological neural network models, image enhancement, image analysis, etc., can solve the problems of blurred image edges, loss of detail information, and reduced effectiveness, and achieve improved denoising effect and easy production. , good denoising effect

Active Publication Date: 2019-07-09
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0003] This method has a simple model and can effectively remove noise with known attributes, but for noise with unknown underwater attributes, the effectiveness of the method will be greatly reduced. serious loss of information

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  • Underwater image denoising method based on generative adversarial network
  • Underwater image denoising method based on generative adversarial network
  • Underwater image denoising method based on generative adversarial network

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the drawings and embodiments.

[0029] In order to overcome the problems that the underwater image noise is difficult to remove and the edge texture cannot be enhanced, the present invention proposes an underwater image denoising method based on a generation of confrontation network. This method first inputs the retina-enhanced underwater image with noise into a generation network composed of several residual blocks to obtain a feature map with three channels (r, g, b) output; then the output The obtained feature map and the noise-free label image of Shimizu are respectively mapped through the VGG-19 network (the network has been proposed and publicly used by Google) to obtain a deep feature space, and the feature map and the noise-free label image of Shimizu are calculated. The perceptual cost in the depth feature space makes the feature map output by the generation network as close as possible to t...

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Abstract

The invention provides an underwater image denoising method based on a generative adversarial network, and the method comprises the steps: inputting an underwater image into a generation network composed of a plurality of residual blocks, and obtaining a feature mapping graph; mapping output feature mapping graph and clear water noiseless label image through VGG-19 network to obtain depth featurespace, calculating the perception cost of the feature mapping graph and a clear water noise-free label image in a depth feature space, calculating the perception cost, inputting the feature mapping graph output by a generation network into an adversarial network, and finally inputting a noise-containing underwater image into the generation network after training is completed, and outputting the noise-free image as a processed noise-free image. According to the method, the confrontation mechanism is introduced, the denoising effect is obvious, particularly, the edge texture information in the image can be effectively reserved and even enhanced through the method, and the better visual effect and imaging quality are achieved.

Description

Technical field [0001] The invention relates to the field of underwater image processing, in particular to a denoising method for underwater images. Background technique [0002] Underwater images play an important role in the development and exploration of marine resources. They are mainly used in the following areas: (1) In the military, it can detect and identify underwater targets, realize underwater high-definition imaging, and facilitate the search and salvage of sunken ships and aircraft wreckage on the seabed. (2) In terms of environmental protection, it can monitor the migration of marine species and changes in the marine ecological environment; (3) In terms of engineering, it can monitor the construction of submarine projects, realize the docking of deep sea workstations, and provide convenience for the subsequent automatic maintenance of the project. Compared with imaging in the air, the environment for taking optical images in water is more complicated. Due to the sc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04
CPCG06T5/002G06T2207/10004G06T2207/20192G06T2207/20081G06N3/045
Inventor 冯晓毅蒋晓悦夏召强李磊黄东张晓彪
Owner NORTHWESTERN POLYTECHNICAL UNIV
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