Implementation of Unsharp Masking Algorithm without Image Processing Toolbox

This guide demonstrates how to implement the unsharp masking algorithm without relying on the Image Processing Toolbox. The process begins by loading an image into the computational environment using standard file I/O functions. The image is then converted to grayscale for processing, typically achieved through RGB-to-grayscale conversion algorithms or by extracting luminance components. Appropriate parameter values must be selected based on image characteristics, including radius for Gaussian blur and amount for sharpening strength. The core algorithm implementation involves three key steps: First, create a blurred version of the original image using Gaussian filtering (implemented through convolution with a Gaussian kernel). Second, subtract this blurred image from the original to obtain the mask component. Third, add a scaled version of this mask back to the original image using the formula: sharpened = original + amount * (original - blurred). This enhances edges and details by emphasizing high-frequency components. Finally, the processed image can be saved using image writing functions or displayed for visual analysis. This straightforward implementation approach allows easy application of unsharp masking to improve image quality through edge enhancement and detail sharpening. The entire process can be coded using basic matrix operations and convolution functions available in standard programming environments.