Deep Dehazing Network With Latent Ensembling Architecture and Adversarial Learning.

Most existing dehazing algorithms recover haze-free image by solving the hazy imaging model using estimated transmission map and global atmospheric light. However, inaccurate estimation of these variables and the strong assumptions of imaging model result in unrealistic dehazing results. In this paper, we use the adversarial game between a pair of neural networks to accomplish end-to-end photo-realistic dehazing. To avoid uniform contrast enhancement, the generator learns to simultaneously restore haze-free image and capture the non-uniformity of haze. The modules for the two tasks are assembled in sequential and parallel manners to enable information sharing at different levels, and the architecture of the generator implicitly forms an ensemble of dehazing models that allows for feature selection. A multi-scale discriminator competes with the generator by learning to detect dehazing artifacts and the inconsistency between dehazed image and the spatial variation of haze. Unlike existing works that penalize dehazing artifacts via hand-crafted loss, the proposed algorithm uses the identity mapping in the space of clear-scene images to regularize data-driven dehazing. The proposed work also addresses the adaptability of data-driven dehazing to high-level computer vision task. We propose a task-driven training strategy that can optimize the object detection performance on dehazed images without updating the parameters of object detector. Performance of the proposed algorithm is assessed on the RESIDE, I-Haze, and O-Haze benchmarks. The comparison with ten state-of-the-art algorithms shows that the proposed work is the best performer in most competitions.