Residual Image

Residual images represent the difference between an original image and a processed or reconstructed version, serving as a crucial element in various image processing and computer vision tasks. Current research focuses on leveraging residual images within deep learning frameworks, particularly employing transformer networks, residual networks (ResNets), and diffusion models to enhance image restoration, compression, and generation, as well as improve the accuracy and efficiency of tasks like object pose estimation and anomaly detection. This approach allows for improved performance in challenging scenarios such as low-light conditions, atmospheric turbulence, and partial data, ultimately impacting fields ranging from medical imaging and astronomy to video processing and security applications.