The recent advent of diffusion models has led to significant progress in
solving inverse problems, leveraging these models as effective generative
priors. Nonetheless, challenges related to the ill-posed nature of such
problems remain, often due to inherent ambiguities in measurements. Drawing
inspiration from the human ability to resolve visual ambiguities through
perceptual biases, here we introduce a novel latent diffusion inverse solver by
incorporating regularization by texts (TReg). Specifically, TReg applies the
textual description of the preconception of the solution during the reverse
sampling phase, of which description isndynamically reinforced through
null-text optimization for adaptive negation. Our comprehensive experimental
results demonstrate that TReg successfully mitigates ambiguity in latent
diffusion inverse solvers, enhancing their effectiveness and accuracy.

通过将预期解决方案的文本描述在逆采样阶段中进行正则化，我们引入了一种创新的潜变扩散逆求解器（TReg），通过感知偏见解决潜变扩散逆求解器中的歧义，从而成功缓解了这类问题的歧义，提高了效果和准确性。