Optimal Transmission Estimation via Fog Density Perception for Efficient Single Image Defogging

Abstract

Single image defogging algorithms based on prior assumptions or constraints have captured much attention because of their simplicity and practicality. However, they still have some challenges to deal with foggy images captured under weather conditions where these assumptions or constraints may not be effective or efficient enough. In this paper, we aim to develop a novel image defogging algorithm by directly predicting the fog density of recovered images rather than adopting prior assumptions or constraints. In order to achieve this goal, two specific steps are introduced. First, we adopt three fog-relevant statistical features derived from foggy images, and further develop a simple fog density evaluator (SFDE) by creating a linear combination of these fog-relevant features. This proposed evaluator can efficiently perceive the fog density of a single image without reference to a corresponding fog-free image and has a low computational load compared with an existing method. Second, a physics-based mathematical relationship between the transmission and the fog density score of the recovered image is developed via SFDE, thus image defogging can be posed as a minimization problem on the fog density score of the recovered image. As a result, two optimal transmission models, called an optimal transmission model via SFDE (OTSFDE) and a simpler optimal transmission models via SFDE (SOTSFDE), are present to determine the key transmission map for efficient fog removal. Compared to OTSFDE, SOTSFDE has low computational complexity with slight performance degradation. Experimental results demonstrate that the proposed algorithms can effectively remove fog and are not confined by any assumptions or constraints, both quantitatively and qualitatively, compared with some existing algorithms.