Nonlinear underwater image gain algorithm for hyperbolic tangent deformation function transformation

Abstract

The invention discloses a nonlinear underwater image gain algorithm for hyperbolic tangent deformation function transformation. The problems of obvious noise pollution, low contrast ratio, low brightness, low saturation, color distortion and the like of underwater optical images due to water transparency, light and the like can be obviously improved through algorithm transformation of the invention. Firstly, an underwater grating image file is read as matrix data, three layers of matrix data of RGB three channels are extracted respectively, a hyperbolic tangent deformation-based function with an expansion coefficient k is constructed, the gain degree and the key gain color gradation range can be controlled through the appropriate expansion coefficient k, and a hyperbolic tangent deformation function is constructed; the function is used to carry out gain transformation on three layers of matrix data of three channels of RGB, and then the three color channels are merged and converted into a new image. The underwater optical image is processed through the algorithm, the contrast and brightness of the underwater image are improved, important color gradation is reasonably redistributed, and the dark area and the color gradation are improved while detail information of the image is reserved and even enhanced.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a nonlinear conversion gain method for underwater optical image digital signals. Background technique [0002] Brightness, contrast, insufficient saturation, fuzziness, noise pollution, color distortion, etc. are common problems in underwater photography or video, and are also the most important reasons that affect image recognition and detection results. There are many reasons for the blurred image, mainly the following four: (1) due to the low transparency of the water body, the exposure is seriously insufficient, resulting in insufficient image brightness, contrast, saturation, and low recognition; (2) the scattering of water causes the image to blur. Reduced definition; (3) Shaking of underwater photography and camera equipment causes image definition to decrease; (4) Inaccurate focus causes image definition to decrease. In view of the above four...

AI Technical Summary

Problems solved by technology

The physical model method is based on the principle of underwater image degradation, and the original image before degradation is reversed. However, this method is limited by the scene parameter assumptions, and the degree of image recovery is limited.

The non-physical model method is derived from the traditional image processing method. According to the underwater image characteristics, the contrast, brightness, color, saturation, and sharpness of the image are corrected at the pixel level, mainly including the space domain method, the transformation domain method, and the color constancy method. , because the imaging features and image content are not considered, the gain process of traditional image processing methods may lose information and introduce interference

In addition, the amount of data used by these two methods is not large, and the generalization ability is difficult to guarantee, and the effect in the field of underwater image and video enhancement is not ideal.

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