In this module, you will examine the fundamental digital image analysis and processing concepts and analytical methods that mainly stem from a digital signal processing approach. Areas discussed include image representation and storage, filtering and transform techniques for image processing including two dimensional Fourier transforms, techniques for noise reduction, image enhancement, coding and compression techniques, and lossy versus lossless compression.

On successful completion of this module, you should be able to: 1. use analytical methods in digital image processing. 2. apply, evaluate and compare various Image Transform models. 3. apply, evaluate and compare various Image Enhancement and Image Restoration techniques. 4. implement different Image Compression techniques. 5. apply the techniques taught in the course to real-life scenarios, interpret the results obtained and propose solutions for further improvements.

Image Transforms: Definition and Properties of 2-D Unitary Transforms. Singular Value Decomposition. Fourier Transform. Discrete Cosine Transform. Walsh Transform. Hadamard Transform. Karhunen-Loeve Transform. Image Enhancement: Histogram manipulation, noise reduction, Principal Component Analysis; Image Restoration: Geometric registration. Inverse filtering. Wiener Filtering. Direct Matrix inversion. Non-linear restoration-Simulated annealing. Edge detection and image thresholding. Texture analysis. Wavelets.