Prior‐guided multiscale network for single‐image dehazing

Abstract

Single-image dehazing is an important problem because it is a key prerequisite for most high-level computer vision tasks. Traditional prior-based methods adopt priors generated from clear images to restrain the atmospheric scattering model and then recover haze-free images. However, these prior-based methods always encounter over-enhancement, such as halos and colour distortion. To solve this problem, many works use a convolutional neural network to retrieve original images. However, without priors as guidance, these learning-based methods dehaze effectively in synthetic datasets but perform poorly in real scenes. Hence, in this paper, we propose a prior-guided multiscale network for single-image dehazing named PGMNet. Specifically, prior-based methods are adopted to acquire dehazed images of the training dataset in advance and then send these dehazed images to a parameter-shared encoder to form multiscale features. During the decoding process, these multiscale features are adopted to guide the prior-guided multiscale network to recover more image details. Moreover, considering that these prior-based dehazed images usually contain some over-enhanced regions, a spatial attention guided feature aggregation module and squeeze-and-excitation module are adopted to alleviate colour distortion. The proposed PGMNet takes the advantage of prior-based methods in real haze removal and provides superior performance compared with the state-of-the-art methods on both synthetic and real-world datasets.