Using neural networks to solve image problems through artificial intelligence

Abstract

Deep neural networks may be utilized to handle a wide range of inverse issues that arise in computational imaging, according to recent machine learning research. We examine the key recurring themes in this developing field and offer a taxonomy that can be applied to group various issues and reconstruction approaches. Our taxonomy is arranged along two main axes in which first includes that if a forward model is known and how much it is utilized in training and testing; and other that whether the learning is supervised or unsupervised, that is, whether the training depends on having access to matched ground truth picture and measurement pairs. The manuscript discusses trade-offs with these various rebuilding strategies, cautions, and typical failure scenarios with potential future research directions in imaging with inverse problems. In addition, the implementation patterns and aspects are integrated with the use of deep convolutional networks in deep learning for inverse problems in imaging.