Learning Robust Imaging Models without Paired Data

Explore robust imaging models without paired data in this 56-minute conference talk from the 44th Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series. Delve into Prof. Chenglong Bao's research on combining classical mathematical modeling with deep neural networks to improve interpretability in image processing. Discover how Bayesian inference frameworks can be leveraged to build AI-aided robust models for applications such as image denoising and segmentation. Examine the challenges of collecting paired training data and learn about innovative approaches to overcome this limitation. Gain insights into linear approximation for imaging processes, error effects, and the data bottleneck in deep learning. Analyze quantitative and qualitative results for real noisy images, including examples from Huawei. Understand the probabilistic model for image segmentation and explore unpaired degradation modeling techniques. Conclude with a summary of experimental findings and visual results demonstrating the effectiveness of these novel approaches in practical imaging systems.