Adaptive gain image enhancement method based on fractional order multi-scale entropy fusion

Abstract

The invention discloses an adaptive gain image enhancement method based on fractional order multi-scale entropy fusion. The adaptive gain image enhancement method comprises the steps: dividing an original underwater image into non-overlapping rectangular image blocks; obtaining enhanced output images which are consistent with the scale number and based on the fractional order; calculating the information entropy and contrast of the fused image; determining an output image with enhanced contrast in the image block; obtaining a global contrast enhanced image; converting the original underwater image from the RGB image into a grayscale image; calculating a gradient image corresponding to the grayscale image, and solving an adaptive gain function of the gradient image; calculating a final adaptive gain underwater enhanced image based on fractional order multilayer entropy fusion; and outputting an adaptive gain underwater enhanced image based on fractional order multilayer entropy fusion.According to the adaptive gain image enhancement method, enhancement processing is carried out on the underwater image, so that details of the enhanced image are richer and clearer, and the contrast ratio, information entropy, color information and the like of the whole image can be further improved.

Description

technical field [0001] The invention belongs to the field of image information processing, and in particular relates to an adaptive gain image enhancement method based on fractional-order multi-scale entropy fusion. 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. Image enhancement is an effective method to process the original image, improve the display effect of the image, and convert it into a more suitable image analysis and processing method. Some scholars have improved the integer-order differential image enhancement. By weighting it or combining other methods, the enhancement effect of the integer-order differential can be improved. However, these methods still cannot effectively improve the shortcomings of integer-order differentials being sensitive to noise and easily suppressing image tex...

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Problems solved by technology

However, these methods still cannot effectively improve the shortcomings of integer-order differentials being sensitive to noise and easily suppressing image texture details.

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