Non Blind

Non-blind image deblurring aims to sharpen blurry images by leveraging known or estimated blur characteristics, unlike blind deblurring which operates without this information. Current research focuses on improving non-blind deblurring techniques using deep learning architectures such as convolutional neural networks, generative adversarial networks, and recurrent networks, often incorporating iterative optimization strategies like Iteratively Reweighted Least Squares (IRLS) for enhanced performance and efficiency. These advancements are significant for various applications, including improving image quality in microscopy, medical imaging, and photography, by effectively removing blur caused by known optical aberrations or motion. The development of robust and efficient non-blind deblurring methods is crucial for enhancing the quality and interpretability of images across numerous scientific and practical domains.