Image Decomposition

Image decomposition involves separating an image into constituent components, such as illumination, texture, and objects, to facilitate analysis, manipulation, or enhancement. Current research focuses on developing efficient and effective decomposition methods using various deep learning architectures, including transformers, convolutional neural networks, and diffusion models, often within a "decompose-and-fuse" paradigm. These advancements are improving image restoration, enhancement (e.g., low-light image enhancement), and analysis tasks across diverse applications, including medical imaging, security, and computer graphics. The ability to decompose images into meaningful components is proving crucial for solving complex problems in various fields.