Motion-Blurred Image Restoration

In this discussion, we can further elaborate on motion-blurred image restoration methods. For images degraded by uniform motion blur, several restoration approaches can be implemented. One primary method involves utilizing image processing algorithms that analyze blur parameters and motion trajectories to reconstruct image clarity and detail. This can be achieved through techniques like deconvolution algorithms (e.g., Wiener filter or Richardson-Lucy deconvolution) where point spread function (PSF) estimation plays a crucial role in determining motion direction and length. Additionally, advanced image restoration technologies such as deep learning and neural networks can significantly enhance restoration quality and precision. Convolutional Neural Networks (CNNs) can be trained end-to-end to learn blur patterns and directly output restored images, while Generative Adversarial Networks (GANs) can produce photorealistic results by learning from paired blurred-sharp image datasets. These restoration methods effectively recover motion-blurred images, enhancing clarity and visual quality to better meet user requirements. Implementation typically involves Python/OpenCV for traditional approaches or TensorFlow/PyTorch frameworks for deep learning solutions, with key functions including PSF estimation, frequency-domain filtering, and neural network inference.