Misty Image Repair System with Dark Channel Prior and CLAHE

Abstract

The aim of this study is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing. Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which reduces the contrast, changes the color, and makes the object features difficult to identify by human vision and by some outdoor computer vision systems. Therefore image dehazing is an important issue and has been widely researched in the field of computer vision. Image haze removal based on dark channel prior can perform very well. But it didn’t deal with sky region and brightness objects because dark channel prior is not established in these regions. To solve the problem and enhance the contrast, this paper propose to restored the hazy image by adding the contrast limited adaptive histogram equalization. With the contrast limited adaptive histogram equalization method, the results of the dark channel prior results can be contrasted so that the image becomes clearer and more specific. Experimental results showed that such method is MSE 0.256 is equal to PSNR of 54,043 With this research, it is expected that a clean image after processing can provide maximum information to humans.