PFONet: A Progressive Feedback Optimization Network for Lightweight Single Image Dehazing.

Image dehazing is an effective means to enhance the quality of images captured in foggy or hazy weather conditions. However, existing image dehazing methods are either ineffective in dealing with complex haze scenes, or incurring too much computation. To overcome these deficiencies, we propose a progressive feedback optimization network (PFONet) which is lightweight yet effective for image dehazing. The PFONet consists of a multi-stream dehazing module and a progressive feedback module. The progressive feedback module feeds the output dehazed image back to the intermedia features extracted by the network, thus enabling the network to gradually reconstruct a complex degraded image. Considering both the effectiveness and efficiency of the network, we also design a lightweight hybrid residual dense block serving as the basic feature extraction module of the proposed PFONet. Extensive experimental results are presented to demonstrate that the proposed model outperforms its state-of-the-art single-image dehazing competitors for both synthetic and real-world images.

Multi-Stream Fusion Network With Generalized Smooth L1 Loss for Single Image Dehazing.

Single image dehazing is an important but challenging computer vision problem. For the problem, an end-to-end convolutional neural network, named multi-stream fusion network (MSFNet), is proposed in this paper. MSFNet is built following the encoder-decoder network structure. The encoder is a three-stream network to produce features at three resolution levels. Residual dense blocks (RDBs) are used for feature extraction. The resizing blocks serve as bridges to connect different streams. The features from different streams are fused in a full connection manner by a feature fusion block, with stream-wise and channel-wise attention mechanisms. The decoder directly regresses the dehazed image from coarse to fine by the use of RDBs and the skip connections. To train the network, we design a generalized smooth L1 loss function, which is a parametric loss family and permits to adjust the insensitivity to the outliers by varying the parameter settings. Moreover, to guide MSFNet to capture the valid features in each stream, we propose the multi-scale supervision learning strategy, where the loss at each resolution level is computed and summed as the final loss. Extensive experimental results demonstrate that the proposed MSFNet achieves superior performance on both synthetic and real-world images, as compared with the state-of-the-art single image dehazing methods.