Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems

• URL: http://arxiv.org/abs/2407.16125v1
• Date: Tue, 23 Jul 2024 02:14:18 GMT
• Title: Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
• Authors: Sojin Lee, Dogyun Park, Inho Kong, Hyunwoo J. Kim,
• Abstract summary: We propose a novel approach to solve inverse problems with a diffusion prior from an amortized variational inference perspective. Our amortized inference learns a function that directly maps measurements to the implicit posterior distributions of corresponding clean data, enabling a single-step posterior sampling even for unseen measurements.
• Score: 12.482127049881026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies on inverse problems have proposed posterior samplers that leverage the pre-trained diffusion models as powerful priors. These attempts have paved the way for using diffusion models in a wide range of inverse problems. However, the existing methods entail computationally demanding iterative sampling procedures and optimize a separate solution for each measurement, which leads to limited scalability and lack of generalization capability across unseen samples. To address these limitations, we propose a novel approach, Diffusion prior-based Amortized Variational Inference (DAVI) that solves inverse problems with a diffusion prior from an amortized variational inference perspective. Specifically, instead of separate measurement-wise optimization, our amortized inference learns a function that directly maps measurements to the implicit posterior distributions of corresponding clean data, enabling a single-step posterior sampling even for unseen measurements. Extensive experiments on image restoration tasks, e.g., Gaussian deblur, 4$\times$ super-resolution, and box inpainting with two benchmark datasets, demonstrate our approach's superior performance over strong baselines. Code is available at https://github.com/mlvlab/DAVI.

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