This document discusses image denoising algorithms and tensor decomposition methods for noise removal. It begins with an introduction to image denoising and different types of noise such as Gaussian, impulse, uniform, and periodic noise. It then describes common image denoising approaches such as spatial filtering and transform domain filtering. Specific algorithms discussed include corner-based filtering, block-based filtering, and tensor decomposition methods like canonical polyadic decomposition and Tucker decomposition. The document provides an overview of these techniques and compares their abilities to remove noise while preserving image detail and structure.