Single Image Defogging via Recurrent Bilateral Learning
Cheng Chen, Wei Liu, Tao Lü · 2022
Image defogging is an important computer vision topic for a long time. Current end-to-end defogging methods based on convolutional neural networks (CNNs) have achieved significant success. However, the inherent ambiguity between scene albedo and depth in foggy degraded images makes foggy image restoration still challenging. In this paper, we introduce a novel fog removal network based on recurrent bilateral learning (RBL) that generates detailed scene depth features and then fully leverages the scene depth to resolve this ambiguity. This network is composed of multiple sets of bilateral grid modules (BGM). In the BGM, we first extract haze-related features in low-resolution streams. Then, the scene depth information is separated from a trainable bilateral grid. Finally, it performs slicing operations for data-dependent lookup in the bilateral grid to reconstruct scene depth with high-frequency features. Moreover, we embed Conv-LSTM in the BGM to better correlate context-relevant information in recurrent structure. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art algorithms.