Gaussian Pyramid - Multi-Scale Image Representation Using Gaussian Pyramid

In digital image processing, the Gaussian pyramid is widely employed for multi-scale image analysis. This pyramid structure consists of a series of images where each level represents the convolution result of the original image with a Gaussian kernel function. The implementation typically involves iterative Gaussian convolution followed by downsampling operations, generating progressively lower-resolution images. This multi-scale representation enables various applications including image scaling, segmentation, and edge detection. In the Gaussian pyramid architecture, each successive level reduces the image dimensions to one-quarter of the previous level (halving both width and height), effectively decreasing the image size by a factor of four per layer. This systematic reduction significantly enhances computational efficiency and analytical accuracy in image processing tasks. From an implementation perspective, key steps involve selecting appropriate Gaussian kernel parameters (like standard deviation) and applying downsampling techniques such as nearest-neighbor or bilinear interpolation after each convolution operation.