MATLAB Implementation of Image Pyramid with Downsampling and Residual Image Computation

In image pyramid processing, we can obtain multi-scale image representations through downsampling operations, while simultaneously generating downsampled images and corresponding residual images. Image pyramids serve as an effective image processing technique that enables analysis and manipulation of images at different resolutions. The implementation typically involves iterative downsampling using Gaussian or Laplacian pyramid algorithms, where each level is created by applying a smoothing filter followed by subsampling. Through pyramid decomposition, we can extract richer image information and better understand image characteristics and structural patterns. In MATLAB, this can be implemented using functions like impyramid for pyramid creation and imresize for scaling operations, with optional Gaussian filtering via imgaussfilt. The residual images (difference between original and reconstructed versions) provide crucial detail information lost during downsampling. Consequently, image pyramids constitute an essential tool in image processing that delivers enhanced image details and multi-scale information, thereby supporting more accurate analysis and decision-making in applications like image compression, object detection, and texture analysis.