Compromised Biophotonic Image Data

Compromised biophotonic image data research focuses on intentionally degrading aspects of image acquisition (e.g., resolution, signal-to-noise ratio) to create faster, cheaper, or more portable imaging systems. Deep learning, particularly neural networks, is then used to computationally reconstruct high-quality images from this deliberately degraded data. This approach is significantly impacting bioimaging by enabling higher temporal resolution and broader accessibility of advanced imaging techniques across various biological applications. The current focus is on optimizing neural network architectures for effective reconstruction while balancing the degree of initial data compromise with the achievable reconstruction quality.