Boundary-constrained robust regularization for single image dehazing

Abstract

Generally, for single image dehazing, regularization-based schemes improve the initial transmission map iteratively by using a guidance map as a structural prior. We conducted experiments on a large number of hazy images and observed that a constrained transmission map affects the quality of the recovered image. However, regularization-based methods do not constrain the transmission map to its physically valid range during the iterative process. It degrades its robustness to outliers, and consequently, deteriorates the quality of the recovered image. In addition, conventional methods fuse the structural information of the guidance and initial transmission map without considering any structural differences between them. To address these issues, in this paper, we present a robust regularization scheme that constraints the transmission map during its enhancement. In the proposed scheme, a nonconvex energy function is constructed that leverages the mutual structural information of the guidance and transmission map. The nonconvex problem is solved by a majorize-minimization algorithm, and the intermediate transmission maps are constrained through the appropriate lower and upper bounds. The retrieved transmission map has better edge-preserving properties, and ultimately, results in a high-quality haze-free image that has faithful colors and fine details. The proposed scheme is tested on benchmark datasets and results are evaluated through quantitative metrics. The comparative analysis has revealed the effectiveness of the proposed scheme.