Introduction: Introduction & Applications, Elements of visual perception, Image sensing and acquisition, simple image formation, Image sampling and Quantization, Representing digital pixels, Image quality, Introduction to colour image – RGB and HSI Models.
Image enhancement in Spatial domain: Introduction to image enhancement, basic grey level transforms, Histogram, Histogram-processing equalization, Matching & colour histogram, Enhancement using arithmetic/logic operations, spatial filtering, Smoothing spatial filtering, Sharpening spatial filtering.
Image Transform: Fourier transform, SHFT, DFT, FFT, DCT, Hadamard Transform, Wavelets transform (CWT, DWT), KLT, SVD, Applications.
Image Enhancement in frequency domain: Smoothing frequency domain filtering, Sharpening frequency domain filtering, A model for Image degradation / restoration process, Noise model, Mean filtering and filtering, estimating degradation function, inverse filtering, minimum mean square error (wiener filter), Colour image smoothening, sharpening.
Segmentation & Morphological operations: segmentation and threshold function, Different algorithms in thresholding, Line detection, Edge detection, Edge linking by graph search method, Hough transform, Region based segmentation, Matching, colour segmentation, Morphological-dilation and erosion, opening and closing, Hit/ miss transforms, Representation Boundary descriptors, Regional descriptors. Image Compression - need for image compression, Huffman, Run length encoding, shift codes, Vector quantization, Transform coding, JPEG standard, MPEG