Single image dehazing with bright object handling

Abstract

This study addresses the shortcomings of the dark channel prior (DCP). The authors propose a new and efficient method for transmission estimation with bright-object handling capability. Based on the intensity value of a bright surface, they categorise DCP failures into two types: (i) obvious failure: occurs on surfaces that are brighter than ambient light. They show that, for these surfaces, altering the transmission value proportional to the brightness is better than the thresholding strategy; (ii) non-obvious failure: occurs on surfaces that are brighter than the neighbourhood average haziness value. Based on the observation that the transmission of a surface is loosely connected to its neighbours, the local average haziness value is used to recompute the transmission of such surfaces. This twofold strategy produces a better estimate of block and pixel-level haze thickness than DCP. To reduce haloes, a reliability map of block-level haze is generated. Then, via reliability-guided fusion of block-and pixel-level haze values, a high-quality refined transmission is obtained. Experimental results show that the authors' method competes well with state-of-the-art methods in typical benchmark images while outperforming these methods in more challenging scenarios. The authors' proposed reliability-guided fusion technique is about 60 times faster than other well-known DCP-based approaches.