Digital Image Processing

4.0 credits

Fundamental concepts, including: two-dimensional Fourier transforms, sampling and quantization, Four slice theorem, image representation and modeling, Shot/Poisson noise in optical images, image perception, tone, color, and frequency response of the visual system, fidelity metrics, basic information theoretic concepts, overview of neural networks and deep learning. Sampling of applications from: compression, filtering, restoration/enhancement, reconstruction and inverse problems, texture modeling, classification, recognition, segmentation, and registration.