Multi Exposure Image Fusion

Multi-exposure image fusion aims to combine multiple images of the same scene taken with different exposure settings into a single, high-dynamic-range image with enhanced detail in both highlights and shadows. Current research emphasizes developing deep learning models, particularly those incorporating transformer architectures and self-supervised learning techniques, to overcome challenges like color distortion and the lack of ground truth data for training. These advancements are improving image quality and efficiency, with applications ranging from mobile photography to medical imaging, where the ability to reconstruct source images from the fused output is crucial for diagnostic purposes.