Building Gaussian Pyramid for Grayscale Images

When processing grayscale images, we can achieve enhanced results by building a Gaussian pyramid while adjusting kernel parameters. This technique involves recursively applying Gaussian smoothing and downsampling operations to create multiple scaled versions of the original image. The implementation typically uses a Gaussian kernel (often with σ=0.5-1.0) for blurring before each downsampling step, effectively reducing image dimensions by half at each pyramid level. Through Gaussian pyramid construction, we can perform image analysis and processing across different scales, which helps capture more detailed features and patterns in the image. Key functions in implementations like OpenCV (pyrDown()) or MATLAB (imgaussfilt() followed by imresize()) facilitate this process by handling the convolution and sampling operations. Therefore, establishing a Gaussian pyramid with optimized kernel parameters proves to be an effective methodology for grayscale image processing tasks.