Light Field Super Resolution

Light field super-resolution (LFSR) aims to enhance the resolution of light field images, which capture both spatial and angular information about a scene, enabling advanced applications like post-capture refocusing. Current research focuses on developing efficient and effective deep learning models, including convolutional neural networks (CNNs), transformers, and diffusion models, to address the computational challenges posed by the high dimensionality of light field data. A key trend is the incorporation of physical priors from the light field imaging process to improve accuracy and generalization, particularly when dealing with real-world, complex degradations. These advancements are crucial for improving the quality and practicality of light field imaging technologies across various applications.