  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.
