Frequency-Guided Posterior Sampling for Diffusion-Based Image Restoration
Image restoration aims to recover high-quality images from degraded observations. When the degradation process is known, the recovery problem can be formulated as an inverse problem, and in a Bayesian context, the goal is to sample a clean reconstruction given the degraded observation. Recently, modern pretrained diffusion models have been used for image restoration by modifying their sampling procedure to account for the degradation process. However, these methods often rely on certain approximations that can lead to significant errors and compromised sample quality. In this paper, we provide the first rigorous analysis of this approximation error for linear inverse problems under distributional assumptions on the space of natural images, demonstrating cases where previous works can fail dramatically. Motivated by our theoretical insights, we propose a simple modification to existing diffusion-based restoration methods. Our approach introduces a time-varying low-pass filter in the frequency domain of the measurements, progressively incorporating higher frequencies during the restoration process. We develop an adaptive curriculum for this frequency schedule based on the underlying data distribution. Our method significantly improves performance on challenging image restoration tasks including motion deblurring and image dehazing.
Code (0)
등록된 구현이 없습니다.
Tasks
DeblurringImage DehazingImage RestorationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations
Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequency content is systematically attenuated, limiting reliability where …
Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference
Zero-shot diffusion posterior sampling offers a flexible framework for inverse problems by accommodating arbitrary degradation operators at test time, but incurs high computational cost due to repeated likelihood-guided …
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
Diffusion models have emerged as a powerful foundation model for visual generation. With an appropriate sampling process, it can effectively serve as a generative prior to solve general inverse problems. Current posterio…
DeblurringDenoisingSuper-ResolutionSplit Gibbs Discrete Diffusion Posterior Sampling
We study the problem of posterior sampling in discrete-state spaces using discrete diffusion models. While posterior sampling methods for continuous diffusion models have achieved remarkable progress, analogous methods f…
Test-Time Anchoring for Discrete Diffusion Posterior Sampling
While continuous diffusion models have achieved remarkable success, discrete diffusion offers a unified framework for jointly modeling text and images. Beyond unification, discrete diffusion provides faster inference, fi…
Question Answering