Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization
This work addresses the computational bottleneck in diffusion-based inverse problem solving for applications like image processing, offering a significant speed-up while maintaining or improving performance.
The paper tackles the inefficiency of diffusion models in solving inverse problems by introducing a plug-and-play module called Measurements Optimization (MO), which accelerates the process and achieves state-of-the-art performance with fewer function evaluations, such as a PSNR of 28.71 dB on FFHQ 256 for high dynamic range imaging using only 100 NFEs compared to 1000-4000 NFEs in existing methods.
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.