Digital Image Processing

General principles and modeling of digital images. Image Perception. Color representation and transformations. 2-D Sampling, 2-D Fourier and other transforms. Image description and processing using vectors and matrix operators. Image enhancement: Histogram equalization and mapping, contrast enhancement, low-pass and highpass filters in two dimensions. Image restoration: Deterministic and stochastic methods. Optimization for the design of image restoration filters, comparisons and applications. Image coding and compression: JPEG, MPEG. Image analysis and segmentation methods

Objectives

By the end of the course, the student will be able to understand and perform tasks that relate to Image Representation, Image Transformations, Image Enhancement, Image Restoration, Image Segmentation

Prerequisites

Discrete-Time Systems, Discrete-time Fourier Transform, Filters

Syllabus

Image Representation, Color Space, Image Sampling, Quantization, Image Quality Enhancement, Discrete Fourier Transform, Frequency-Domain Filtering, Image Transform, Image Restoration, Edge Detection, Image & Video Compression