Foggy weather image enhancement method based on fractional differential and dark channel prior

Abstract

The invention discloses a foggy weather image enhancement method based on fractional differential and dark channel prior. The method herein includes the following steps: 1. inputting a foggy weather image I, conducting dark channel prior and Retinex algorithm processing on the I, obtaining an initial de-foggy image J (x,y); 2. segmenting the J (x,y) to a foreground image J1(x,y) and a background J2(x,y); 3. separately computing the optimal fractional differential order number v1 corresponding to J1(x,y) and the optimal fractional differential order value v2 corresponding to J2(x,y); 4. determining a mask coefficient and a mask size, constructing a fractional differential operator mask w (s,t); 5. separately introducing the fractional differential order number v1 and fractional differential order value v2 which are obtained from 3 to the w (s,t), obtaining w1(s,t) and w2(s,t), conducting convolution operation on the pixel points of w1(s,t) and J1(x,y), and conducting convolution operation on the pixel points of w2(s,t) and J2(x,y); and 6. outputting the image of I which after the image enhancement. According to the invention, the method herein addresses poor de-foggy effects which are often caused in the process of enhancing foggy images by using fractional differential algorithm which has single fractional differential order in the prior art.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a foggy image enhancement method based on fractional differential and dark channel prior. Background technique [0002] Image enhancement is one of the important image preprocessing technologies. Image enhancement can improve the image quality and visual effect of the image for subsequent in-depth processing, such as image segmentation, edge extraction, and pattern recognition, all of which are image enhancement methods. Usually, due to the influence of sunlight or light source, the image acquired by the imaging device has low contrast and low definition, and the detailed texture information of the local image is not obvious, which brings difficulties to the in-depth processing of various images. Especially in conditions of low visibility, it is difficult for surveillance equipment to capture high-quality images. The acquired images are blurry and the overall color is dark...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a foggy image enhancement method based on fractional differential and dark channel prior, which solves the problem in the prior art of enhancing foggy images with a fractional differential algorithm with a single fractional differential order , the problem of poor defogging effect

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