Frequency-guidance Collaborative Triple-branch Network for single image dehazing

Abstract

Image dehazing is a crucial pre-processing step in many high-level vision applications, e.g., crowd surveillance, automatic driving, and remote sensing. Recently, learning-based methods have achieved promising performance by designing various convolutional neural networks (CNNs). However, most existing CNNs are based on a single feature branch and cannot fully utilize the low-frequency and high-frequency features of haze, which inevitably affects the quality of the restored image. To this end, we develop an effective Frequency-guidance Collaborative Triple-branch Network (FCT-Net) for image dehazing. It casts dehazing as a dual-frequency component restoration task and deals with pertinent features based on a triple-branch-like architecture. Specifically, our FCT-Net can be decomposed into four components: low-frequency branch (LFB), high-frequency branch (HFB), progressive fusion branch (PFB), and image reconstruction tail (IRT). The LFB aims to mine more contextual information with dual-path feature modulation block (DFMB), while the HFB aims to recover the missing structural detail via mixed attention residual block (MARB). PFB is a fusion branch built between LFB and HFB, where different feature streams (low- and high-frequency information) can be integrated together through continuous selective progressive fusion (SPF) modules. Finally, our IRT exploits all these derived features to generate the clear haze-free image. Furthermore, we utilize a multi-strategy loss to optimize our FCT-Net from the perspectives of pixel domain and feature domain, which can achieve better image recovery quality. Extensive experiments on different benchmark datasets show that our FCT-Net is able to get comparable results with other state-of-the-art methods.