# Syllabus
## Unit I: FUNDAMENTALS OF IMAGE PROCESSING AND IMAGE TRANSFORMS

Introduction, Image sampling, Quantization, Resolution, Elements of image processing system, Applications of Digital image processing. Need for transform, image transforms, Fourier transform, 2 D Discrete Fourier transform, Discrete cosine transform, Walsh transform, Hadamard transform, Haar transform, slant transform and KL transform.
## Unit II: IMAGE ENHANCEMENT
Spatial domain methods: Point & Histogram processing, Fundamentals of Spatial filtering, Smoothing spatial filters, Sharpening spatial filters.
Frequency domain methods: Basics of filtering in frequency domain, image smoothing, image sharpening, Selective filtering.
## Unit III: IMAGE RESTORATION AND RECONSTRUCTION
A model of the image degradation and Restoration process, Noise models, restoration in the presence of noise only-Spatial Filtering, Periodic Noise Reduction by frequency domain filtering, Linear, Position –Invariant Degradations, Estimating the degradation function, Inverse filtering, Minimum mean square error (Wiener) filtering, constrained least squares filtering, geometric mean filter, image reconstruction from projections.
## Unit IV: IMAGE FEATURE EXTRACTION
#### Image Segmentation
Fundamentals, point, line, edge detection, thresholding, and region –based segmentation.
#### Morphological Image Processing
Preliminaries, Erosion and dilation, opening and closing, basic morphological algorithms for boundary extraction, thinning.
## Unit V: IMAGE COMPRESSION
Introduction, Need for image compression, Redundancy in images, Classification of redundancy in images, image compression scheme, Classification of image compression schemes, Fundamentals of information theory, Run length coding, Shannon – Fano coding, Huffman coding, Arithmetic coding, Predictive coding, Transformed based compression, Image compression standard.