281 research outputs found
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild
Diffusion models have shown promising results on single-image
super-resolution and other image- to-image translation tasks. Despite this
success, they have not outperformed state-of-the-art GAN models on the more
challenging blind super-resolution task, where the input images are out of
distribution, with unknown degradations. This paper introduces SR3+, a
diffusion-based model for blind super-resolution, establishing a new
state-of-the-art. To this end, we advocate self-supervised training with a
combination of composite, parameterized degradations for self-supervised
training, and noise-conditioing augmentation during training and testing. With
these innovations, a large-scale convolutional architecture, and large-scale
datasets, SR3+ greatly outperforms SR3. It outperforms Real-ESRGAN when trained
on the same data, with a DRealSR FID score of 36.82 vs. 37.22, which further
improves to FID of 32.37 with larger models, and further still with larger
training sets
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