This chapter deals with image processing and contains theoretical discussions and MATLAB coding examples, including Basic MATLAB functions and Image Processing Toolbox (IPT) functions, related to the following topics: reading and writing image data, image-type conversions, binarization threshold and Otsu’s method, image quantization and gray levels, indexed image and dithering, image representations using unsigned integer 8-bits and double precision 64-bits, image color representations, color models and colormaps, device-dependent and device-independent models, tristimulus and chromaticity values, sRGB and AdobeRGB color spaces, color conversion between RGB, CMY, XYZ, HSV and L*a*b* representations, synthetic images like checkerboard and phantom, image noise representations and Gaussian functions, basic display techniques, image fusion and montage, image sequences and warping surfaces, interactive exploration tools, common geometric transformations, affine and projective transformations, image registrations, covariance and correlation, zooming and interpolation, kernel, convolution, image blurring, noise filters, order statistic filters, Gabor filter, edge detection operators, image gradients and partial derivatives of images, contrast adjustment and gamma curves, histogram equalization, morphological operations, region-of-interest and block processing, arithmetic and logical operations, point spread function, inverse filter, Wiener deconvolution, Lucy–Richardson deconvolution, blind deconvolution, image segmentation, object analysis, Hough transform, quad-tree decomposition, extracting region properties, pixel connectivity, texture analysis, gray-level co-occurrence matrix, image quality, signal to noise ratio, mean square error, structural similarity index, image transforms, discrete Fourier transform, discrete cosine transform, discrete wavelet transform, convolution theorem, ideal and Gaussian low-pass and high-pass filters, development of Simulink models for image processing tasks, various 2-D and 3-D plotting functions for visualization of data distributions, and their customization aspects.