Multi Exposure

Multi-exposure imaging aims to overcome the limitations of camera sensors by combining multiple images of the same scene taken with different exposure settings to create a high dynamic range (HDR) image with improved detail and color accuracy. Current research focuses on developing efficient deep learning models, often employing encoder-decoder architectures, transformers, or convolutional neural networks, to fuse these images while minimizing artifacts like ghosting and computational cost, particularly for mobile applications. These advancements are significant for improving image quality in various applications, from mobile photography to satellite imagery and 3D HDR video, by enabling the capture and processing of more realistic and detailed visual information.