Image superreconstruction for Micro-CT based on compressed sensing
压缩感知在Micro-CT图像超分辨重建中的应用

In Micro-CT systems,further improvement of the spatial resolution of the reconstructed images is limited by the X-ray dose level,the pixel pitch,and the aperture of the detector element.In this paper,we study a total variation (TV) based optimization model for Micro-CT reconstruction based on an up-sampling of the reconstruction grid with original detector and X-ray dose.Using an extension of the gradient projection method,an alternating minimization algorithm is employed to solve the corresponding energy function.In the process of the minimization,the treatment is separated into the gradient step of the fit-to-data term,the total variation (TV) denoising,and the specific linear combination of the previous two points.Experiments on simulated data as well as real Micro-CT data are performed.Our results show that the proposed approach can dramatically improve the spatial resolution of the reconstructed images compared to the conventional Filter Back-Projection algorithm.