[Quantum-ms] Mark Your Calendar 10/11: EE Seminar - Design Light-weight Deep Networks for Diverse Image Restoration Tasks

[Columbia EE Events]

*EE Seminar Announcement *

*Designing light-weight deep networks for diverse image restoration tasks
<https://www.ee.columbia.edu/events/ee-seminar-designing-light-weight-deep-networks-diverse-image-restoration-tasks>*

*Prof. Se Young Chun*

*Time: 4-5pm, Oct/11/2024*

*Location: 750 CEPSR, Columbia University*



*Abstract:*

Since the advent of deep learning, image enhancement was one of the first
applications of it to outperform classical algorithms. Large models usually
perform better in image restoration tasks, but it is often desirable to
achieve excellent performance with small networks, especially for embedded
systems. In this talk, I will go over some of the works where my Lab has
designed small networks for diverse image restoration tasks such as
progressive single image deblurring model (ECCV 2020), all-in-one model for
multiple degradations (CVPR 2023) and its extension to image demosaicing
for modern non-Bayer image sensors (ICCV 2023) as well as our recent work
on pretraining-tuning architecture based on LoRA, but with flexible ranks
for efficiency (ECCV 2024).



*Bio:*

Se Young Chun received his Ph.D. degree in Electrical Engineering: Systems
from the University of Michigan, Ann Arbor in 2009. He is currently a
Professor in the Department of Electrical and Computer Engineering and the
Interdisciplinary Program in AI, Seoul National University, South Korea. He
is an associate editor of IEEE Transactions on Image Processing and IEEE
Transactions on Computational Imaging as well as a member of IEEE Bio
Imaging and Signal Processing Technical Committee. He was the recipient of
the 2015 Bruce Hasegawa Young Investigator Medical Imaging Science Award
from the IEEE Nuclear and Plasma Sciences Society. His research­ interests
include computational imaging algorithms using deep learning and
statistical signal processing for applications in medical imaging and
computer vision.
[image: chun.jpg]
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