mlvlab/DAVI
Official Implementation (Pytorch) of "DAVI: Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems", ECCV 2024 Oral paper
This project helps image restoration professionals efficiently reconstruct high-quality images from noisy or incomplete measurements. It takes a degraded image (e.g., blurry, low-resolution, or with missing parts) and quickly outputs a clear, complete, and restored version. Scientists, medical imaging specialists, and digital artists would find this useful for tasks like deblurring, super-resolution, inpainting, denoising, and colorization.
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Use this if you need to restore multiple types of degraded images, such as blurry photos, low-resolution scans, or images with missing sections, with high efficiency and without needing to optimize for each new image.
Not ideal if your image restoration needs are outside of the supported degradation types (deblurring, super-resolution, inpainting, denoising, colorization) or if you require real-time processing on embedded devices.
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Language
Python
License
MIT
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Last pushed
Aug 16, 2024
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