Spectral Dual-Channel Encoding for Image Dehazing

In recent years, deep learning-based dehazing models have presented a momentum of dramatic growth. Unfortunately, most deep learning-based approaches heavily rely on synthetically hazed images for model training, which makes these methods brittle to restore hazy images taken from real-world scenes, due to the sample distribution discrepancy between synthetic and realistic images. Although some attempts have been made to overcome this difficulty by augmenting image spatial features with spectral features, the power of the spectral features still remains underutilized. In this paper, we propose the Spectral Dual-Channel Encoding (SDCE) framework for high-quality image dehazing, by unleashing the power of spectral feature encoding. We argue that hazes impose more adverse impacts on high-frequency image features (e.g., outlines and textures) than low-frequency features (e.g., colors), with theoretical and empirical justifications. To better restore hazed high- and low-frequency features, we decompose the hazed images into high- and low-frequency feature components with spectral dual-channel encoding and respectively design effective neural network architectures to recover hazed images on the two feature components. To be specific, we recover the low-frequency feature components with an encoder-decoder, while we specially design a high-frequency aggregation component (HFAC) to recover hazed images on high-frequency feature components, by referring to neighboring feature distributions. We conduct extensive experiments on four real-world image dehazing benchmarks. The experimental results show that our proposed SDCE framework outperforms the state-of-the-art baselines significantly, with an average 4.4% improvement in PSNR and an average 7.7% gain in SSIM.