HDR Reconstruction

High dynamic range (HDR) reconstruction aims to create images or videos with a far wider range of brightness levels than standard displays can handle, capturing details in both very bright and very dark areas. Current research focuses on improving the accuracy and speed of HDR reconstruction from various input sources, including single images, multi-exposure sequences, and even event-based camera data, employing deep learning models like Gaussian splatting and specialized networks designed for ghost artifact suppression and detail preservation. These advancements are significant for improving image quality in various applications, such as computational photography, virtual and augmented reality, and computer vision tasks that benefit from accurate scene representation.