基于多级服从像素网络鉴别器（DIAMOND）的深度迭代医学图像恢复
Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery
摘要：
图像复原是一个典型的不适定问题，它包含了多种任务。在医学成像领域中，不适定图像中断诊断，甚至中断后续的图像处理。传统的迭代和最新的深度网络都引起了广泛的关注，并在重建令人满意的图像方面取得了显着的进步。本研究将它们的优点结合到一个统一的数学模型中，并提出了一种通用的图像恢复策略来处理这些问题。该战略包括两个模块。首先，构建了一种新的具有WGAN-GP训练的生成对抗网络（GAN）来恢复图像结构和细微细节。然后，深度迭代模块通过ADMM优化利用预训练的深度网络和压缩感知算法的组合来提升图像质量。（D）eep（I）迭代模块抑制图像伪影并进一步恢复微妙的图像细节，（A）由（M）多级（O）超像素特征提取网络（D）识别器支持以恢复一般结构。因此，该策略被命名为DIAMOND。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
