Algorithm unrolling is a technique that connects iterative algorithms used in signal and image processing with deep neural networks, offering interpretable and efficient architectures. This method transforms iterative steps into network layers, enabling the training of networks that mimic the behavior of traditional algorithms while leveraging the power of deep learning. By unrolling, the network can be optimized using end-to-end training, leading to improved performance and interpretability. The technique has been applied in various domains, including computational imaging, medical imaging, and vision and recognition tasks. In computational imaging, unrolling has been used for tasks like single image super-resolution and blind image deblurring, where it has shown significant improvements in performance and efficiency. In medical imaging, unrolling has been applied to MRI and CT reconstruction, where it has enhanced the quality of reconstructed images with fewer parameters. In vision and recognition, unrolling has been used to improve the accuracy and efficiency of image processing tasks. The method has also been shown to be effective in handling limited training data and improving generalization. Overall, algorithm unrolling provides a promising approach to developing interpretable and efficient deep learning models for signal and image processing.Algorithm unrolling is a technique that connects iterative algorithms used in signal and image processing with deep neural networks, offering interpretable and efficient architectures. This method transforms iterative steps into network layers, enabling the training of networks that mimic the behavior of traditional algorithms while leveraging the power of deep learning. By unrolling, the network can be optimized using end-to-end training, leading to improved performance and interpretability. The technique has been applied in various domains, including computational imaging, medical imaging, and vision and recognition tasks. In computational imaging, unrolling has been used for tasks like single image super-resolution and blind image deblurring, where it has shown significant improvements in performance and efficiency. In medical imaging, unrolling has been applied to MRI and CT reconstruction, where it has enhanced the quality of reconstructed images with fewer parameters. In vision and recognition, unrolling has been used to improve the accuracy and efficiency of image processing tasks. The method has also been shown to be effective in handling limited training data and improving generalization. Overall, algorithm unrolling provides a promising approach to developing interpretable and efficient deep learning models for signal and image processing.