# Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization

Source: [Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization](<https://www.sota2.com/research/paper/68fa760e1e209131cea6b2bc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:46:34.345Z

Authors: Tianyu Chen, Zhendong Wang, Mingyuan Zhou

Published: 2024-12-05T07:44:18.000Z

## Abstract

Diffusion models have recently demonstrated notable success in solving inverse problems. However, current diffusion model-based solutions typically require a large number of function evaluations (NFEs) to generate high-quality images conditioned on measurements, as they incorporate only limited information at each step. To accelerate the diffusion-based inverse problem-solving process, we introduce \\textbf{M}easurements \\textbf{O}ptimization (MO), a more efficient plug-and-play module for integrating measurement information at each step of the inverse problem-solving process. This method is comprehensively evaluated across eight diverse linear and nonlinear tasks on the FFHQ and ImageNet datasets. By using MO, we establish state-of-the-art (SOTA) performance across multiple tasks, with key advantages: (1) it operates with no more than 100 NFEs, with phase retrieval on ImageNet being the sole exception; (2) it achieves SOTA or near-SOTA results even at low NFE counts; and (3) it can be seamlessly integrated into existing diffusion model-based solutions for inverse problems, such as DPS \\cite{chung2022diffusion} and Red-diff \\cite{mardani2023variational}. For example, DPS-MO attains a peak signal-to-noise ratio (PSNR) of 28.71 dB on the FFHQ 256 dataset for high dynamic range imaging, setting a new SOTA benchmark with only 100 NFEs, whereas current methods require between 1000 and 4000 NFEs for comparable performance.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.03941>)

[PDF](<https://arxiv.org/pdf/2412.03941.pdf>)