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.