Image Restoration via Frequency Selection
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain by considering the large discrepancy between spectra of sharp/degraded image pairs. However, these algorithms commonly utilize transformation tools, e.g. , wavelet transform, to split features into several frequency parts, which is not flexible enough to select the most informative frequency component to recover. In this paper, we exploit a multi-branch and content-aware module to decompose features into separate frequency subbands dynamically and locally, and then accentuate the useful ones via channel-wise attention weights. In addition, to handle large-scale degradation blurs, we propose an extremely simple decoupling and modulation module to enlarge the receptive field via global and window-based average pooling. Furthermore, we merge the paradigm of multi-stage networks into a single U-shaped network to pursue multi-scale receptive fields and improve efficiency. Finally, integrating the above designs into a convolutional backbone, the proposed Frequency Selection Network (FSNet) performs favorably against state-of-the-art algorithms on 20 different benchmark datasets for 6 representative image restoration tasks, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, image deraining, and image denoising.
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Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Image Deblurring | GoPro | FSNet | PSNR | 33.29 | # 12 | |
SSIM | 0.963 | # 14 | ||||
Image Dehazing | Haze4k | FSNet | PSNR | 34.12 | # 3 | |
SSIM | 0.99 | # 2 | ||||
Image Dehazing | SOTS Indoor | FSNet | PSNR | 42.45 | # 3 | |
SSIM | 0.997 | # 1 | ||||
Image Dehazing | SOTS Outdoor | FSNet | PSNR | 40.40 | # 2 | |
SSIM | 0.997 | # 1 |