Synchrotron microtomography image restoration via regularization representation and deep CNN prior. (November 2022) - Li, Yimin; Han, Shuo; Zhao, Yuqing; Li, Fangzhi; Ji, Dongjiang; Zhao, Xinyan; Liu, Dayong; Jian, Jianbo; Hu, Chunhong
Highlights: We fully utilized the advantages of both regularization and deep learning and embedded them into the PnP framework to develop a novel image restoration model. The proposed method can handle structural noise and random noise simultaneously. An effective augmented Lagrange multiplier (ALM) algorithm is designed to optimize the proposed image restoration model. Abstract: Background and objective: Synchrotron-based X-ray microtomography (S-µCT) is a promising imaging technique that plays an important role in modern medical science. S-µCT systems often cause various artifacts and noises in the reconstructed CT images, such as ring artifacts, quantum noise, and electronic noise. In most situations, such noise and artifacts occur simultaneously, which results in a deterioration in the image quality and affects subsequent research. Due to the complexity of the distribution of these mixed artifacts and noise, it is difficult to restore the corrupted images. To address this issue, we propose a novel algorithm to remove mixed artifacts and noise from S-µCT images simultaneously. Methods: There are two important aspects of our method. Regarding ring artifacts, because of their specific structural characteristics, regularization-based methods are more suitable; thus, low-rank tensor decomposition and total variation are utilized to represent their directional and locally piecewise smoothness properties. Moreover, to determine the implicit prior of the random noise, a convolutional neural network (CNN) based method is used. The advantages of traditional regularization and the deep CNN are then combined and embedded in a plug-and-play framework. Hence, an efficient image restoration algorithm is proposed to address the problem of mixed artifacts and noise in S-µCT images. Results: Our proposed method was assessed by utilizing simulations and real data experiments. The qualitative results showed that the proposed method could effectively remove ring artifacts as well as random noise. The quantitative results demonstrated that the proposed method achieved almost the best results in terms of PSNR, SSIM and MAE compared to other methods. Conclusions: The proposed method can serve as an effective tool for restoring corrupted S-µCT images, and it has the potential to promote the application of S-µCT.