Go to ICLR 2025 Conference homepageMasked, Regularized Fidelity With Diffusion Models For Highly Ill-posed Inverse Problems
Diffusion models have been well-investigated for solving ill-posed inverse problems to yield excellent performance. However, their application to highly ill-posed inverse problems remains challenging. In this work, we propose zero-shot diffusion model for large and complex kernels, dubbed Dilack, incorporating novel data fidelity terms. Based on our analyses on the ill-posedness for challenging inverse problems, we propose regularized fidelity called pseudo-inverse anchor for constraining (PiAC) fidelity loss. Inspired by locally acting classical regularizers, we also propose to incorporate masked fidelity within PiAC loss that can interact with globally acting diffusion models, which adaptively enforces spatially and step-wisely local fidelity via masks. Our proposed scheme effectively reduces erratic behavior and inherent artifacts in diffusion models, thereby improving restoration quality including perceptual aspects and outperforming prior arts on both synthetic and real-world datasets for modern lensless imaging and large motion deblurring.