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Image restoration is a typical ill-posed problem, and it contains varioustasks. In the medical imaging field, an ill-posed image interrupts diagnosisand even following image processing. Both traditional iterative and up-to-datedeep networks have attracted much attention and obtained a significantimprovement in reconstructing satisfying images. This study combines theiradvantages into one unified mathematical model and proposes a general imagerestoration strategy to deal with such problems. This strategy consists of twomodules. First, a novel generative adversarial net(GAN) with WGAN-GP trainingis built to recover image structures and subtle details. Then, a deep iterationmodule promotes image quality with a combination of pre-trained deep networksand compressed sensing algorithms by ADMM optimization. (D)eep (I)terationmodule 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 strategyis named DIAMOND.

Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery