Image Dehazing Based on High-Frequency and Low-Frequency Feature Enhancement

Abstract

Image dehazing is a typical ill-posed problem.Dehazing network architectures commonly use encoder-decoder networks consisting of encoders, decoders, and feature converters that connect the two.The quality of dehazing images generated by existing dehazing algorithms is generally low, with problems such as incomplete local detail dehazing, color distortion, or the introduction of noise.Dehazing algorithms based on an encoder-decoder network do not fully utilize small-scale features when designing feature converters, and only use the corresponding layer coding features in the decoding stage; therefore, this paper proposes a dehazing algorithm based on high-frequency and low-frequency feature enhancement.In the feature transformation stage, an expanded residual component is designed and formed into a context aggregation network, which makes full use of the low-resolution features of the large receptive field, extracts the remote correlation of feature maps, and enhances low-frequency small-scale features.Furthermore, a multi-level feature reuse network based on channel attention is designed to reuse shallow high-frequency features while deeply fusing decoded and reconstructed features to enhance the recovery of visual perceptual features.In the coding stage, a Visual Feature Perception Module(VFPM) is constructed to enhance the rich high-frequency visual features of the shallow layer by leveraging the advantages of residual blocks in local modeling.Experimental results show that compared with AOD-Net, PFF-Net, and other dehazing algorithms, the Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity(SSIM) of the proposed algorithm have significant advantages.The PSNR and SSIM of the proposed algorithm are 0.77 dB and 0.000 7, and 0.40 dB and 0.037 1 higher than those of the other algorithms, respectively, on an indoor synthetic dataset, SOTS, and outdoor real dataset, Dense-Haze.