Three Filters for Image Processing: Laplacian, Unsharp Mask, and High-Boost Filtering

In image processing, three commonly used filters are the Laplacian filter, Unsharp Mask filter, and High-Boost filter, each playing critical roles in enhancing image quality. The Laplacian filter primarily serves for edge detection and enhancement by highlighting high-frequency components through a second-derivative operator, typically implemented using a 3x3 convolution kernel like [[0,1,0],[1,-4,1],[0,1,0]]. The Unsharp Mask filter effectively reduces blurring effects by subtracting a blurred version of the image from the original, often achieved through Gaussian blur convolution followed by weighted subtraction to enhance local contrast. The High-Boost filter amplifies image details and sharpness by emphasizing high-frequency information, essentially functioning as an amplified version of the Unsharp Mask with a adjustable gain factor (k>1) in the formula: High-Boost = Original + k*(Original - Blurred). These filters enable diverse image manipulations through discrete convolution operations, allowing developers to achieve optimal results based on specific application requirements.