Exposure Fusion

Exposure fusion aims to combine multiple images of the same scene taken with different exposure settings to create a single image with a wider dynamic range and improved detail in both highlights and shadows. Current research focuses on developing efficient algorithms, often employing deep learning architectures like convolutional neural networks and transformers, to achieve real-time performance, particularly for mobile applications, and to address challenges like motion blur and noise. These advancements are significant for improving image quality in various applications, from computational photography and panoramic stitching to 3D modeling and enhancing the accuracy of scene surveys.