Design And Implementation Of Fog-degraded Image Adaptive Image Sharpening

In the conditions of fog, haze and other weather, the image contrast is reduced and quality decline, owing to scatter of small particles in the atmosphere. This not only to extract the image features cause interference, but also make the outdoor surveillance system cannot reliably. Therefore, the study of the effective and fast de-hazing method, making fog degraded image sharpen, has important practical significance.This paper first introduces the atmospheric scattering model, analyzes the physical cause of fog degraded image degradation, and studies in depth of the image de-haze algorithm based on dark channel prior. In order to solve the problem of high computational complexity of soft matting, this paper proposes an improved multi-scale minimum filter to optimize the transmission. The experiments show the improved algorithm can increase the running speed of the algorithm, with keeping the image de-hazing effect.The result image obtained from the image de-haze algorithm based on the dark channel prior has problems of low brightness, low contrast and indistinct low details. In order to solve this problem, this paper proposed contrast limited adaptive histogram equalization algorithm to adjust the contrast, experiments show this algorithm can enhancement the contrast of image and improve the image brightness.The result image obtained from the image enhancement algorithm based on the multi-scale Retinex has problems of low efficiency and low brightness. In order to solve this problem, this paper proposed luminance component Retinex enhancement under HSV color space, and using Laplace operator to detect the edge of image, this algorithm improved the operating efficiency and obtained the good defogging effect.This paper introduced the measure standard fog concentration to classify the fog degraded image in outdoor scene, and constructed the system of fog degraded image adaptive sharpening, this system make full use of the advantages and disadvantages of the proposed algorithm, realized adaptive sharpen processing, the experimental verified the validity and practicability of the proposed algorithm.