Image Processing I: Composition, Pooling, Continuity, and Denoising

This lecture covers the properties of composition in image processing, including preservation of linearity and convolutional structure. It also discusses pooling techniques for down-sampling and continuity requirements for operators. The concept of Lipschitz continuity and stability in module combinations are explored, along with denoising methods such as Resnet and DnCNN architecture. The presentation concludes with an overview of popular CNN architectures for image segmentation, focusing on U-net and its applications in biomedical image analysis.