This lecture introduces digital image processing, defining a digital image as a 2D array of quantized intensity values. It details the conversion of analog images to digital via sampling and quantization, explaining spatial and intensity resolution. Applications like image enhancement, restoration (de-blurring, noise reduction), segmentation, and object recognition are discussed, along with different image types (binary, grayscale, RGB) and color models. The lecture also covers distance metrics (Euclidean, city block, chessboard) and image operations (addition, subtraction, translation, rotation, scaling).