Blind Deconvolution

Blind deconvolution aims to recover a sharp image or signal from a blurred, noisy version when the blurring process is unknown. Current research heavily utilizes deep learning, employing architectures like generative adversarial networks (GANs) and recurrent neural networks to learn effective image priors and improve optimization strategies, often incorporating classic iterative methods like Landweber or Richardson-Lucy for enhanced performance. These advancements are impacting diverse fields, from astronomical imaging and microscopy to bearing fault diagnosis and text detection in images, by enabling more accurate and efficient signal/image restoration. The development of robust and computationally efficient methods remains a key focus, particularly in handling spatially varying blur and high noise levels.