Regularization by Texts for Latent Diffusion Inverse Solvers

arXiv:2311.15658v2 Announce Type: replace-cross
Abstract: The recent advent of diffusion models has led to significant progress in solving inverse problems, leveraging these models as effective generative priors. Nonetheless, there remain challenges related to the ill-posed nature of such problems, often due to inherent ambiguities in measurements or intrinsic system symmetries. To address this, drawing inspiration from the human ability to resolve visual ambiguities through perceptual biases, here we introduce a novel latent diffusion inverse solver by regularization by texts (TReg). …

abstract arxiv challenges cs.ai cs.cv cs.lg diffusion diffusion models generative intrinsic nature progress regularization type