Abstract:\u00a0\u2002\u2002\u2002\u2002<\/strong><\/p>\nStructured and spatially correlated noise presents a major challenge in scientific and biomedical imaging, where idealized assumptions of additive white Gaussian noise often break down. This dissertation addresses this challenge through two frameworks based on deep learning for solving inverse problems with structured noise: a simulation-based supervised learning approach for low signal-to-background ratio (SBR) fluorescence imaging, and a novel generative modeling framework based on Whitened Score (WS) diffusion models for general imaging inverse problems with correlated Gaussian noise.<\/p>\n
The first part of this work introduces SBR-Net, a deep neural network trained on synthetic data generated by a structured background noise simulator that models light scattering and structured fluorescent background in thick biological tissue. This approach enables single-shot 3D volumetric reconstruction from light-field microscopy measurements with extremely low SBR. By explicitly modeling structured background noise and simulating realistic measurement\u2013ground truth pairs, SBR-Net learns a direct inverse mapping. The framework is evaluated on synthetic and experimental data, with analysis of generalization behavior under real-world noise mismatch.<\/p>\n
The second part introduces Whitened Score (WS) diffusion models, a new class of generative priors tailored to inverse problems with structured noise. Conventional score-based diffusion models, trained on isotropic Gaussian noise, lack inductive biases suitable for real-world noise distributions encountered in applications such as diffraction tomography, interferometry, and wide-field microscopy. WS models reformulate the denoising objective by learning a whitened score function, thus avoiding covariance inversion and enabling training under arbitrary Gaussian forward processes. This formulation allows WS models to serve as strong Bayesian priors, denoising structured noise and consistently outperforming conventional diffusion models across a range of computational imaging tasks.<\/p>\n
These contributions highlight the importance of aligning priors with real-world data and incorporating physical models and domain-specific noise characteristics to address inverse problems in realistic imaging settings. By combining simulation, supervised learning, and generative modeling, this work offers robust, interpretable solutions under structured and spatially correlated noise conditions.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Congratulations, Jeffrey! Title:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Deep Learning Approaches For Imaging Inverse Problems With Structured Noise Presenter:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Jeffrey Alido Date:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Friday, June 27, 2025 Time:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a01:30 pm – 2:30 pm Location:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a08 St. Mary’s St. PHO 428 Advisor:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Professor\u00a0Lei Tian Chair:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Professor Tianyu Wang Committee:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Professor\u00a0Lei Tian,\u00a0Professor\u00a0Vivek Goyal,\u00a0Professor\u00a0Eshed Ohn-Bar,\u00a0Professor\u00a0Kayhan Batmanghelich,\u00a0Professor\u00a0Yu Sun (ECE, Johns Hopkins University Google Scholar Link:\u00a0https:\/\/scholar.google.com\/citations?user=zoI7oukAAAAJ&hl=en Abstract:\u00a0\u2002\u2002\u2002\u2002 Structured and spatially correlated […]<\/p>\n","protected":false},"author":12228,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/2440"}],"collection":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/users\/12228"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/comments?post=2440"}],"version-history":[{"count":2,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/2440\/revisions"}],"predecessor-version":[{"id":2442,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/2440\/revisions\/2442"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/media?parent=2440"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/categories?post=2440"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/tags?post=2440"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}