Image Inversion

Image inversion aims to reconstruct an image's underlying representation from its observed form, often addressing challenges like noise, blur, or modality differences. Current research focuses on improving inversion accuracy and speed using various deep learning architectures, including transformers and diffusion models, often incorporating iterative refinement techniques and physics-based constraints to enhance realism and editability. These advancements are impacting diverse fields, from enhancing visual SLAM systems and enabling realistic image editing to improving medical image analysis and solving inverse problems in other scientific domains. The development of more efficient and robust inversion methods continues to be a significant area of investigation.