ECE Seminar: Tianao Li

ECE Seminar: Tianao Li

Title: Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMM

Abstract: Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies. This ill-posed linear inverse problem is usually solved with deconvolution algorithms enhanced by regularisation priors or deep learning. We introduce a so-called "physics-informed deep learning" approach to the Point Spread Function (PSF) deconvolution problem in galaxy surveys. We apply algorithm unrolling and the Plug-and-Play technique to the Alternating Direction Method of Multipliers (ADMM), in which a neural network learns appropriate hyperparameters and denoising priors from simulated galaxy images. We characterize the time-performance trade-off of several methods for galaxies of differing brightness levels as well as our method's robustness to systematic PSF errors and ablations. We show improvements in reduced shear ellipticity error at all SNR levels compared to classic and modern methods.

Bio: Tianao Li is a first-year Ph.D. student in Computer Science at Northwestern University, advised by Prof. Emma Alexander. Previously, he received his B.Eng. in Electronic Engineering at Tsinghua University in 2023. His research interest is in the field of computational imaging, which lies at the intersection of optics, signal processing, computer vision, computer graphics, and machine learning. Specifically, he enjoys solving imaging inverse problems using domain-specific knowledge with scientific applications.