Image Processing: Neighborhood Averaging & Gaussian Smoothing

This lecture covers the concepts of neighborhood averaging and Gaussian smoothing in image processing. Neighborhood averaging involves replacing each pixel with the average of itself and its neighbors, while Gaussian smoothing uses a set of weights approximating a Gaussian function to reduce noise. The lecture also discusses the application of median filters for noise reduction and the iterative median filtering process. Additionally, it explores contrast enhancement techniques, including linear and non-linear manipulation, as well as edge detection methods such as Laplacian sharpening and Sobel operator. Practical examples and applications of these techniques are demonstrated.