Image Super-Resolution Via Iterative Refinement

We present SR3, an approach to image Super-Resolution via Repeated Refinement. We evaluate SR3 on a 4× super-resolution task on ImageNet, where SR3 outperforms baselines in human evaluation and classification accuracy of a ResNet-50 classifier trained on high-resolution images.

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• 1 SR3 adapts denoising diffusion probabilistic models (Ho and colleagues
• 2 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process.
• 3 Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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