From Pixels to Perception: Generative and Adaptive Methodologies for Degraded Visual Scenarios

PhD Thesis Proposal Defence


Title: "From Pixels to Perception: Generative and Adaptive Methodologies for 
Degraded Visual Scenarios"

by

Mr. Qiang WEN


Abstract:

Enhancing images and videos in degraded visual scenarios is a vital task in 
computer vision, with applications across diverse fields such as medical 
imaging, robotics, and photography. Existing works have focused heavily on 
improving pixel-level quality in the final enhanced images through 
techniques like denoising, deblurring, and deraining. However, increasing 
evidence shows that while these pixel-level enhancement methods produce 
visually appealing results for human sense, they may not be optimal for 
downstream-task perception, such as object detection and semantic 
segmentation, which are essential for applications like robotics and 
autonomous driving.

In this proposal, we aim to explore image and video enhancement from 
pixel-level enhancement for human sense to perception-level enhancement 
tailored for downstream computer vision tasks. This will be achieved by 
developing innovative enhancement systems leveraging cutting-edge 
methodologies, such as generative models and domain adaptation.Specifically, 
this proposal explores four representative tasks: waterdrop removal for 
driving scenes in rainy scenarios, low-light image enhancement with 
generative models, enhancing HDR imaging with joint denoising and 
deblurring, adapting large multi-modal models to see and read in the dark. 
With extensive experiments, it gradually highlights the distinction between 
pixel-level enhancement for human sense and perception-level enhancement for 
downstream computer vision tasks, providing a clearer direction for future 
research on enhancement objectives.


Date:                   Tuesday, 6 May 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 2408
                        Lifts 17/18

Committee Members:      Dr. Qifeng Chen (Supervisor)
                        Prof. Nevin Zhang (Chairperson)
                        Prof. Qiong Luo