Joint Image

Joint image processing encompasses techniques that simultaneously analyze and manipulate multiple aspects of an image or multiple images, aiming to improve accuracy, efficiency, or extract richer information than single-image methods allow. Current research focuses on developing novel algorithms and architectures, such as transformer-based networks and normalizing flows, to address challenges in diverse applications including 3D generation, visual place recognition, and low-light/low-resolution image enhancement. These advancements are improving the accuracy and robustness of image analysis across various fields, from medical imaging (e.g., precise quantification of joint space narrowing) to robotics and computer vision (e.g., improved camera and subject registration). The resulting improvements in image processing have significant implications for both scientific understanding and practical applications.