Underwater image denoising method based on generative adversarial network

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...

AI Technical Summary

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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