Underwater image deblurring method based on generative adversarial network

Abstract

The invention discloses an underwater image deblurring method based on a generative adversarial network, and the method comprises the steps: obtaining an underwater degraded image and a clear image as training samples, training an improved DeblurGAN network, and obtaining an optimization model; inputting an underwater degraded image needing to be processed into the trained optimization model to obtain a clear image; and comparing the obtained clear image with the original degraded image, and analyzing and summarizing the accuracy of the optimization model. The resolution of the generated image is further improved by improving the learning ability of the network, and the details of the image are further extracted, so that the image features are detailed and enriched as far as possible, the loss of feature information is avoided, the details of the generated processed image are more perfect on the basis of deblurring, and the image quality is improved. Noisy points of the underwater image can be effectively removed, the problem of detail blurring can be effectively solved, and the deblurring effect of the underwater image is improved.

Description

technical field [0001] The invention relates to the technical fields of deep learning and image processing, in particular to an underwater image deblurring method based on a generative confrontation network. Background technique [0002] Underwater images are an important carrier of marine information, and theoretical technologies such as acquisition, transmission and processing of marine information are crucial to the rational development, utilization and protection of marine resources. However, due to the complex underwater imaging environment and the absorption and scattering of light when it propagates underwater, underwater images often appear color cast, low contrast, blur, and uneven illumination. Therefore, the images obtained by people directly shooting on the seabed are often degraded images, and the degraded images cannot meet the needs of practical applications, which seriously affects the further use of underwater images. Improvements to improve picture clarity...

AI Technical Summary

Problems solved by technology

Although there are many existing underwater image enhancement methods, most of them still have certain limitations, and it is difficult to restore both the color and clarity of the image.

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