Image Defogging Based on Regional Gradient Constrained Prior

Abstract

Foggy days limit the functionality of outdoor surveillance systems. However, it is still a challenge for existing methods to maintain the uniformity of defogging between image regions with a similar depth of field and large differences in appearance. To address above problem, this article proposes a regional gradient constrained prior (RGCP) for defogging that uses the piecewise smoothing characteristic of the scene structure to achieve accurate estimation and reliable constraint of the transmission. RGCP first derives that when adjacent similar pixels in the fog image are aggregated and spatially divided into regions, clusters of region pixels in RGB space conform to a chi-square distribution. The offset of the confidence boundary of the clusters can be regarded as the initial transmission of each region. RGCP further uses a gradient distribution to distinguish different regional appearances and formulate an interregional constraint function to constrain the overestimation of the transmission in the flat region, thereby maintaining the consistency between the estimated transmission map and the depth map. The experimental results demonstrate that the proposed method can achieve natural defogging performance in terms of various foggy conditions.