Joint Transmission Map Estimation and Dehazing Using Deep Networks

• Published in: IEEE transactions on circuits and systems for video technology 2020-07, Vol.30 (7), p.1975-1986
• Main Authors: Zhang, He, Sindagi, Vishwanath, Patel, Vishal M.
• Format: Article
• Language: English
• Subjects: Atmospheric modeling; Degradation; Dehazing; Estimation; Feature extraction; generative adversarial network; Generative adversarial networks; Haze; Ill posed problems; Image transmission; Learning; low-level vision; Propagation losses; Task analysis
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2019.2912145

Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved visually appealing results. However, most of the existing methods assume constant atmospheric light model and tend to follow a two-step procedure involving prior-based methods for estimating transmission map followed by calculation of dehazed image using the closed form solution. In this paper, we relax the constant atmospheric light assumption and propose a novel unified single image dehazing network that jointly estimates the transmission map and performs dehazing. In other words, our new approach provides an end-to-end learning framework, where the inherent transmission map and dehazed result are learned jointly from the loss function. The extensive experiments evaluated on synthetic and real datasets with challenging hazy images demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.