Exploring the Potential of Pooling Techniques for Universal Image Restoration

Image restoration involves recovering a clean image from its degraded counterpart. In recent years, we have witnessed a paradigm shift from convolutional neural networks to Transformers, which have quadratic complexity with respect to the input size. Instead of designing more complex modules based on recent techniques, this paper presents an efficient and effective mechanism for image restoration by exploring the potential of ubiquitous pooling techniques. We leverage different pooling operators as tools for implicit dual-domain representation learning. Specifically, the average and max pooling can be used as extractors for implicit low- and high-frequency signals, respectively. Then, we utilize lightweight learnable parameters to modulate the resulting frequency components. Furthermore, the intermediate high-frequency features can serve as attention maps to highlight the spatial edge information. Our pooling module is built by incorporating the aforementioned dual-domain modulation across multiple scales and various shapes. We demonstrate the effectiveness of our module in single-degradation, composite-degradation, and all-in-one image restoration tasks. Extensive experimental results show that the resulting network achieves state-of-the-art performance on 15 datasets for five single-degradation and two composite-degradation image restoration tasks by deploying our module. Moreover, our method can be extended to all-in-one scenarios and performs favorably against state-of-the-art all-in-one algorithms under two settings.