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Regularization by Texts for Latent Diffusion Inverse Solvers

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arxiv 2311.15658 v3 pith:7DMS3E6P submitted 2023-11-27 cs.CV cs.AIcs.LG

Jeongsol Kim , Geon Yeong Park , Hyungjin Chung , Jong Chul Ye This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords diffusioninverseproblemstregambiguitiesdescriptionslatentmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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The recent development of diffusion models has led to significant progress in solving inverse problems by leveraging these models as powerful generative priors. However, challenges persist due to the ill-posed nature of such problems, often arising from ambiguities in measurements or intrinsic system symmetries. To address this, here we introduce a novel latent diffusion inverse solver, regularization by text (TReg), inspired by the human ability to resolve visual ambiguities through perceptual biases. TReg integrates textual descriptions of preconceptions about the solution during reverse diffusion sampling, dynamically reinforcing these descriptions through null-text optimization, which we refer to as adaptive negation. Our comprehensive experimental results demonstrate that TReg effectively mitigates ambiguity in inverse problems, improving both accuracy and efficiency.

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Cited by 5 Pith papers

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    Using clinical metadata as text prompts in a diffusion prior yields 0.2 to 0.5 dB PSNR gains for compressed sensing MRI reconstruction, but the gains are inconsistent at some acceleration factors.

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