Nighttime Single Image Dehazing Based on the Structural Patch Decomposition

Abstract

Images acquired from the outdoors are often degraded owing to bad weather conditions, such as haze, fog, and rain. Various algorithms for dehazing daytime images have been introduced to eliminate the effects of haze. However, unlike daytime conditions, where sunlight is the main source of light, there are multiple regional light sources in nighttime conditions. Thus, it is difficult to effectively eliminate the effects of haze at night using dehazing algorithms that primarily target daytime hazy images. In addition, most nighttime dehazing algorithms adopt the dark channel prior (DCP) to estimate the transmission, but this approach has problems due to the spatially variant illuminations. In this paper, we propose a nighttime single image dehazing algorithm based on the structural patch decomposition. First, atmospheric light is estimated by separating the intensity and color of light. To estimate the transmission, the candidates for the transmission values are set, and the final transmission value is obtained based on a weighted summation of these candidates for each image pixel. The weights of the candidates are determined according to the structural patch information of the scene radiances estimated with these candidates. Using this approach, an appropriate transmission map can be obtained, and the dehazing procedure is shown to be robust to the mixed illumination conditions observed in the nighttime conditions. The experimental results show that the proposed algorithm effectively eliminate the effect of haze in images based on quantitative and qualitative evaluations.INDEX TERMS Nighttime dehazing, atmospheric scattering model, visibility restoration, contrast enhancement.

DSSCNet: Deep Custom Spatial and Spectral Consistency Layer-Based Dehazing Network

“…Addressing specific challenges, like nighttime dehazing, has spurred the development of specialized methods [23], [24]. These methods take into account the unique characteristics of low-light conditions and adaptively estimate atmospheric point spread functions.…”