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Learning to dehaze single hazy images, especially using a small trainingdataset is quite challenging. We propose a novel generative adversarial networkarchitecture for this problem, namely back projected pyramid network (BPPNet),that gives good performance for a variety of challenging haze conditions,including dense haze and inhomogeneous haze. Our architecture incorporateslearning of multiple levels of complexities while retaining spatial contextthrough iterative blocks of UNets and structural information of multiple scalesthrough a novel pyramidal convolution block. These blocks together for thegenerator and are amenable to learning through back projection. We have shownthat our network can be trained without over-fitting using as few as 20 imagepairs of hazy and non-hazy images. We report the state of the art performanceson NTIRE 2018 homogeneous haze datasets for indoor and outdoor images, NTIRE2019 denseHaze dataset, and NTIRE 2020 non-homogeneous haze dataset.

Single image dehazing for a variety of haze scenarios using back projected pyramid network