2024/06/24 by Rémi Laumont, Yiqiu Dong, Laumont, Remi +3 · 1 citation
Computer Science · Engineering · #62C10 #62F15 #65C05 #65D18 #65K05 #65K10 #68Q25 #68U10 #90C25 #Blind Source Separation Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Image and Video Processing (eess.IV) #Statistics Theory (math.ST) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.16658
openalex publication_date 2024/06/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper studies two classes of sampling methods for the solution of inverse problems, namely Randomize-Then-Optimize (RTO), which is rooted in sensitivity analysis, and Langevin methods, which are rooted in the Bayesian framework. The two classes of methods correspond to different assumptions and yield samples from different target distributions. We highlight the main conceptual and theoretical differences between the two approaches and compare them from a practical point of view by tackling two classical inverse problems in imaging: deblurring and inpainting. We show that the choice of the sampling method has a significant impact on the quality of the reconstruction and that the RTO method is more robust to the choice of the parameters.