Laplacian Pyramid

The Laplacian pyramid is a multi-scale image decomposition technique that separates an image into a set of progressively coarser approximations and corresponding detail images. Current research focuses on leveraging this decomposition within deep learning architectures, such as convolutional neural networks and autoencoders, for applications like image compression, tone mapping, shadow removal, and inpainting. These applications benefit from the pyramid's ability to efficiently represent both global context (low frequencies) and fine details (high frequencies), leading to improved performance and computational efficiency in various image processing tasks. The resulting models often demonstrate superior accuracy and speed compared to traditional methods, impacting fields ranging from computer vision to industrial quality control.