Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery
This paper addresses the problem of ill-posed medical image recovery, which can interrupt diagnosis and subsequent image processing, for medical professionals. The proposed method is an incremental approach combining existing techniques.
The paper proposes DIAMOND, a general image restoration strategy that combines a novel generative adversarial network (GAN) with WGAN-GP training to recover image structures and subtle details, and a deep iteration module that uses pre-trained deep networks and compressed sensing algorithms via ADMM optimization to suppress artifacts and further recover details. The study aims to improve medical image recovery, which is crucial for diagnosis and subsequent image processing.
Image restoration is a typical ill-posed problem, and it contains various tasks. In the medical imaging field, an ill-posed image interrupts diagnosis and even following image processing. Both traditional iterative and up-to-date deep networks have attracted much attention and obtained a significant improvement in reconstructing satisfying images. This study combines their advantages into one unified mathematical model and proposes a general image restoration strategy to deal with such problems. This strategy consists of two modules. First, a novel generative adversarial net(GAN) with WGAN-GP training is built to recover image structures and subtle details. Then, a deep iteration module promotes image quality with a combination of pre-trained deep networks and compressed sensing algorithms by ADMM optimization. (D)eep (I)teration module suppresses image artifacts and further recovers subtle image details, (A)ssisted by (M)ulti-level (O)bey-pixel feature extraction networks (D)iscriminator to recover general structures. Therefore, the proposed strategy is named DIAMOND.