Underwater image enhancement method fusing deep learning and traditional image enhancement technology

Abstract

The invention discloses an underwater image enhancement method fusing deep learning and a traditional image enhancement technology, which comprises the following steps of: firstly, analyzing an average value difference between each channel of an input underwater image and a corresponding natural image, and showing that a red channel needs to be compensated and a green channel needs to be attenuated from the difference; therefore, the color compensation is performed on the R channel and the G channel of the input underwater image by using an attention-guided residual network, and the motivation of the strategy is that most of the underwater images are observed to be composed of relatively single and uniform color distribution. For scene contrast enhancement and scene deblurring, a multi-scale convolutional neural network is developed, and a CLAHE (Contrast Adaptive Histogram Equalization) and a Gamma correction algorithm are introduced as supplements to process a complex and changeable underwater imaging environment. Experimental results show that the underwater image enhancement method has a good effect.

Description

technical field [0001] The invention relates to the technical field of image enhancement, in particular to an underwater image enhancement method integrating deep learning and traditional image enhancement technology. Background technique [0002] The visual quality of underwater images is of great significance for the understanding and recognition of underwater scenes. However, due to the selective absorption and scattering of light by water, underwater images captured by underwater imaging devices usually suffer from severe color distortion, blurring, low contrast and low brightness, which seriously affects people's understanding of underwater scenes. and perception. [0003] For underwater image enhancement, in the past few years, many different methods have been proposed to improve the visual quality of underwater images. These methods can be roughly divided into underwater image enhancement methods based on traditional image processing, image restoration methods based...

AI Technical Summary

Problems solved by technology

Despite significant progress, deep learning-based underwater image enhancement methods are still somewhat challenging due to the complexity of underwater imaging degradation mechanisms, including loss of local details and global color distortion of entire image pixels.

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