A gradient domain self-adaptive gain underwater image enhancement method based on a YIQ space optical imaging model

Abstract

The invention discloses a gradient domain adaptive gain underwater image enhancement method based on a YIQ space optical imaging model. The gradient domain adaptive gain underwater image enhancement method comprises the following steps: carrying out global background illumination vector estimation; obtaining the optimized transmissivity; The blocky effect of the transmissivity is eliminated; calculating a de-noising recovery image corresponding to the original observation image; converting the original color underwater image into a grayscale image; converting the grayscale image into a grayscale image capable of keeping image edge characteristics, and extracting a gradient image corresponding to the image; fusing the de-noised restored image and the gray level image which maintains the edge characteristics; converting the fused color image into a color space, and separating the brightness information from the color information; calculating the brightness information to obtain an enhanced image; converting the enhanced image into a color space. The four-direction Sobel edge detector used in the invention can fully utilize rich gradient information of the image to realize image enhancement processing, so that the visual quality of the processed image is improved, and the texture information is rich.

Description

technical field [0001] The invention belongs to the field of image information processing, and in particular relates to a gradient domain adaptive gain underwater image enhancement method based on a YIQ spatial optical imaging model. Background technique [0002] Underwater target detection images often have special conditions such as non-uniform brightness, low signal-to-noise ratio, and low contrast, which lead to serious degradation of underwater images. Commonly used underwater target detection image enhancement algorithms are mainly divided into modifying the illumination of underwater images and suppressing There are two categories of image contrast to preserve image edges, but it will inevitably reduce the visual quality of the detected image. Traditional image enhancement algorithms based on contrast enhancement have great limitations. When the image contrast is improved, the loss of image information may be ignored, and image color distortion may be caused, and new ...

AI Technical Summary

Problems solved by technology

[0002] Underwater target detection images often have special conditions such as non-uniform brightness, low signal-to-noise ratio, and low contrast, which lead to serious degradation of underwater images. Commonly used underwater target detection image enhancement algorithms are mainly divided into modifying the illumination of underwater images and suppressing Image contrast to preserve the two categories of image edges, but it will inevitably reduce the visual quality of the detected image

The traditional image enhancement algorithm based on contrast enhancement has great limitations. When the image contrast is improved, the image information loss may be ignored, and the image color may be distorted, and new noise may even be introduced, etc.

Due to the optical characteristics of the complex underwater environment and the influence of various particles, plankton, and water flow in the water, the effect of directly translating and transferring the research results of the atmospheric scattering target imaging model for underwater detection image enhancement is still insufficient.

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