Laplacian Pyramid Transform: Image Decomposition and Reconstruction Algorithm

In image processing, the Laplacian pyramid transform is a widely used technique for image compression and reconstruction. This algorithm decomposes an image into multiple representations at different resolutions, where each level captures varying degrees of detail and blur characteristics. For instance, higher-resolution layers preserve fine pixel-level details, while lower-resolution versions exhibit reduced detail and sharpness. The reconstruction process leverages these multi-resolution images to reconstruct the original image through progressive refinement. Implementation typically involves Gaussian filtering for downsampling and upsampling operations, with Laplacian layers storing the difference between adjacent resolution levels. Key functions include Gaussian pyramid generation (using cv2.pyrDown()/pyrUp() in OpenCV) and difference calculation for compact representation. This technique finds extensive applications in digital image processing, computer vision systems, and image recognition pipelines for efficient multi-scale analysis.