Image Deconvolution

Image deconvolution aims to recover a sharp, high-resolution image from a blurred observation, a crucial task across diverse fields like medical imaging, astronomy, and microscopy. Current research emphasizes developing advanced algorithms and model architectures, including deep learning approaches (e.g., convolutional neural networks, generative adversarial networks, and transformer-based models), often incorporating physics-based priors or classical methods like Richardson-Lucy deconvolution for improved accuracy and efficiency. These advancements are significantly impacting various scientific domains by enhancing image quality, enabling more precise measurements, and facilitating the analysis of complex data in applications ranging from medical diagnosis to astronomical observations.