Simple Baselines for Image Restoration

By Liangyu Chen, Xiaojie Chu, X. Zhang, Jian‐Yuan Sun. European Conference on Computer Vision, 2022

1,592 citations recorded by the source corpus.

Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods. In this paper, we propose a simple baseline that exceeds the SOTA methods and is computationally efficient. To further simplify the baseline, we reveal that the nonlinear activation functions, e.g. Sigmoid, ReLU, GELU, Softmax, etc. are not necessary: they could be replaced by multiplication or removed. Thus, we derive a Nonlinear Activation Free Network, namely NAFNet, from the baseline. SOTA results are achieved on various challenging benchmarks, e.g. 33.69 dB PSNR on GoPro (for image deblurring), exceeding the previous SOTA 0.38 dB with only 8.4% of its computational costs; 40.30 dB PSNR on SIDD (for image denoising), exceeding the previous SOTA 0.28 dB with less than half of its computational costs. The code and the pre-trained models are released at https://github.com/megvii-research/NAFNet.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7d4c2c8407e0caf2f907df9954b056a42a92fd13

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Citations in the corpus

Image Restoration with Mean-Reverting Stochastic Differential Equations (2023, background): “Various CNN-based architectures have been proposed (Za-mir et al., 2021; Chen et al., 2022), and recently the use of transformers has also been extensively explored (Liang et al., 2021; Zamir et al., 2022; Luo et al., 2022b).”
MambaIR: A Simple Baseline for Image Restoration with State-Space Model (2024, background): “In addition, progress continues to be made in designing efficient attention for restoration [9–12,26,38,63,72,78,85].”
Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring (2022, methodology): “Therefore, the deblurred results by the meth-ods [3,4] still contain signiﬁcant blur effect as shown in Figure 3(c) and (g).”
Dual Aggregation Transformer for Image Super-Resolution (2023, result): “Our DAT-light achieves better performance with fewer Params and FLOPs for all scale factors, compared with SwinIR and ELAN.”
DiffIR: Efficient Diffusion Model for Image Restoration (2023, methodology): “Presently, the transformer has been adopted in numerous vision tasks, such as image recognition [17, 60], segmentation [62, 69, 87, 49], object detection [5, 89], and image restoration [7, 38, 74, 36, 8].”