Low Dynamic Range Image

Low dynamic range (LDR) images, limited in their ability to capture the full range of light intensities present in a scene, are a significant challenge in image processing. Current research focuses on improving LDR image quality through techniques like multi-scale exposure fusion, neural augmentation, and diffusion models, often leveraging deep learning architectures such as convolutional neural networks and generative adversarial networks to reconstruct high dynamic range (HDR) information or enhance LDR images directly. These advancements aim to improve image quality, enabling better representation of real-world scenes and facilitating applications such as panoramic stitching, HDR video reconstruction, and improved feature point detection for computer vision tasks. The resulting improvements in image fidelity have broad implications for various fields, including photography, computer vision, and virtual/augmented reality.