Computational Imaging

Computational imaging leverages computational algorithms to overcome limitations of traditional optical systems, aiming to improve image quality, resolution, and acquisition speed. Current research heavily emphasizes the integration of deep learning, particularly convolutional neural networks (CNNs) and implicit neural representations (INRs), alongside physics-based models to solve inverse problems in various imaging modalities, including microscopy, tomography, and light-field imaging. These advancements are driving progress in diverse fields like biomedical imaging, astronomy, and mobile vision, enabling higher-resolution imaging with reduced hardware complexity and improved robustness to noise and artifacts. The development of large, standardized datasets is also crucial for training and evaluating these advanced computational imaging algorithms.