OBJECTIVE: Image Processing is an advanced course offered as elective- Aims to stimulate interest in learning theory, techniques and applications of image processing.
PRE-REQUISITE: Data Structures, Computer Graphics, Mathematics at degree level including Algebra and Transformations.
I. DIGITAL IMAGE FUNDAMENTALS 
Introduction — Image Representation -- Steps in Image Processing — Elements of image Processing Sampling and Quantization — Relationships between pixels — Imaging Geometry.
2. IMAGE TRANSFORMS 
Fourier, Discrete Fourier, Fast Fourier. Walsh, Hadamard, Discrete Cosine and Haar transforms.
3. IMAGE ENHANCEMENT AND RESTORATION 
Domain methods - Point processing Filtering — Color Image Processing — Deradation Model —Circulant and Block Circulant matrices — Restoration — Inverse Filtering.
4. IMAGE COMPRESSION AND CODING 
Redundancy — Compression models — Coding Theorems — Different types of Co — Lossy and Losseless compression - Compression Standards.
5. IMAGE SEGMENTATION 
Detection of Discontinuities — Boundary Detection — Edge linking — Thresholding Segmentation — Image representation — Morphology — Interpretation.
1. R. GONZALEZ and R. E. WOOD, Digital Image Processing, Prentice Hall of India 1992.
1. A. ROSENFELD and A.C.KAK, Digital Picture Processing, Prentice Hall Internatonal, 1982.
2. W.K.PRATT, Digital Image Processing, McGraw Hill, 1981.
3. A.C. ANDREWS and B.R. HUNT, Digital Image Restoration. Prentice Hall International, 1990.