This paper provides a comprehensive overview of image modeling techniques over the past six decades, tracing their evolution from handcrafted mathematical priors to modern deep foundation image models. It unifies these approaches under a Bayesian framework, viewing image modeling as a probabilistic characterization of a data manifold. The paper highlights how both traditional methods like Tikhonov and TV, and contemporary techniques such as Plug-and-Play priors and Denoising Diffusion models, can be understood through the lens of the gradient of the log-prior or the Stein Score Function. This perspective bridges the gap between signal processing and machine learning, demonstrating a continuous progression in image modeling driven by the pursuit of denoising scores. AI
IMPACT Provides a unified theoretical framework for understanding and developing advanced image generation and restoration models.
RANK_REASON Academic paper detailing a unified framework for image modeling. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Bayes' theorem
- Denoising diffusion-weighted magnitude MR images using rank and edge constraints
- Hugging Face
- map
- plug and play
- P versus NP problem
- television
- Tikhonov
- total variation
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