DmitryUlyanov/deep-image-prior resource

Health v2 · maintenance only

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8088 stars · 1440 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Jupyter Notebook reference implementations of the CVPR 2018 'Deep Image Prior' paper, which restores images using randomly-initialized neural networks as a structured prior without any training. It includes notebooks reproducing denoising, super-resolution, and inpainting results, runnable locally, via Docker, or on Google Colab.

Use cases

• remove noise from images without training a model
• inpaint missing regions of a photo
• upscale images with super-resolution
• reproduce deep image prior paper figures
• restore images without a training dataset
• experiment with neural network priors for inverse problems

When to choose

• you want training-free image restoration using a random network prior
• you want to reproduce or build on the Deep Image Prior paper
• you have a GPU and prefer notebook-based experimentation

When to avoid

• you need a production-ready image restoration library or API
• you want fast inference - the method is slow per-image optimization
• you need guaranteed convergence on all GPUs, as some Tesla GPUs have known issues

Facets

learning-resource · maturity maintenance

image-processing machine-learning deep-learning computer-vision image-processing deep-learning artificial-intelligence python cross-platform image-restoration denoising inpainting super-resolution jupyter-notebooks research-code pytorch cvpr-2018 gpu docker

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