Deep Prior

Deep priors leverage the power of deep neural networks to incorporate prior knowledge into inverse problems, improving the accuracy and efficiency of solutions compared to traditional methods. Current research focuses on developing and applying deep priors within various frameworks, including plug-and-play regularization, differentiable physics models, and Bayesian approaches, often utilizing convolutional neural networks or graph neural networks for efficient computation. This approach has shown significant promise across diverse fields, such as medical imaging, radar imaging, and audio processing, by enabling improved reconstruction quality, faster computation, and enhanced robustness to noise and incomplete data. The resulting advancements are impacting various scientific disciplines and practical applications by providing more accurate and efficient solutions to complex inverse problems.