A Novel Imagedehazing Network with a Parallel Attention Block Abstract   Image dehazing is a very important preprocessing step to many computer vision tasks such as object recognition and tracking. However, it is a challenging problem because the physical parameters of imaging, e.g. the depth information of scene pixels and the attenuation model, are usually unknown. Based on a physical model, different methods have been proposed to recover these parameters. Existing convolutional neural networks (CNNs) based methods try to solve the image dehazing problem using an endtoend network to learn a direct mapping between a hazy image and its corresponding clear image. But the representational ability, spatial variant ability and dehazing capability of these network models are hindered by treating all the spatial and channelwise features indiscriminately. Hence, we propose an endtoend dehazing network with a parallel spatialchannelwise attention block for capturing more informative spatial and channelwise features respectively. Specifically, based on the encoderdecoder framework with a pyramid pooling operation, a novel parallel spatialchannelwise attention block is proposed and applied to the end of the encoder for guiding the decoder to reconstruct better clear images. In the spatialchannelwise attention block, the spatial attention module and the channelwise attention module are connected in parallel, where the spatial attention module highlights important spatial positions of features. Meanwhile, the channelwise module exploits interdependencies among the channelwise features. Extensive experiments demonstrate that our network with a parallel spatial channelwise attention block can achieve better accuracy and visual results over stateoftheart methods.

A convex single image dehazing model via sparse dark channel prior Abstract   In this paper, we present a convex model for single image dehazing via a sparse dark channel prior. Our work is based on an observation that the number of bright pixels is very small in the dark channel of a hazefree image, but significantly increases in that of a hazy image due to the existence of bright atmosphere light. Since the dehazing problem is inherently ambiguous, we first reformulate the degradation model of hazy images into an equivalent form where the transmission and the image variable are decoupled. With the above formulation and observation, we propose the convex model to recover the hazefree image whose dark channel is assumed to be sparse. The proposed objective function consists of four terms: a datafitting term, an l1 regularization term for the dark channel of the hazefree image, and two total variation regularization terms for both the hazefree image and the transmission map. We develop an efficient alternating direction method of multipliers (ADMM) to tackle the proposed convex model. Extensive experiments on real hazy images illustrate that our method outperforms the stateoftheart methods.