Algorithm for Image Gaussian Blur

The Gaussian blur algorithm is a fundamental image processing technique that creates a softening effect by reducing image noise and detail. This algorithm allows users to customize both the kernel size (the convolution matrix dimensions) and the blur radius (sigma value controlling the spread of the Gaussian distribution). The implementation typically involves convolving the image with a Gaussian kernel, where each pixel's value is recalculated as a weighted average of its neighboring pixels based on the Gaussian function. Key implementation steps include generating the Gaussian kernel matrix using the formula G(x,y) = (1/(2πσ²)) * e^(-(x²+y²)/(2σ²)), normalizing the kernel values, and applying convolution through nested loops or optimized matrix operations. Through proper parameter adjustment, Gaussian blur can enhance image quality by removing noise while preserving essential features, making it crucial for professionals in computer vision and image processing applications. We hope this technical explanation proves valuable for your implementation needs.