Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing

Recently, low-dose computed tomography （CT） has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms, compressed sensing-based iterative re- construction has achieved excellent imaging performance, but its clinical application is hindered due to its computational ineffi- ciency. To promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation mini- mization and sparse dictionary learning to enhance the reconstruction performance, and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency, which validates its potential for low-dose CT imaging.

Fringe contrast enhancement of digital off-axis hologram via sparse representation

This Letter presents a novel approach to enhance the fringe contrast（visibility） in a digital off-axis hologram digitally, which can save several adjustment procedures. In the approach, we train a pair of coupled dictionaries from a low fringe contrast hologram and a high one of the same specimen, use the dictionaries to sparse code the input hologram, and finally output a higher fringe contrast hologram. The sparse representation shows good adaptability on holograms. The experimental results demonstrate the benefit of low noise in a three-dimensional profile and prove the effectiveness of the approach.

Three-dimensional sound source localization using distributed microphone arrays

To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing（CS） theory.In this method two-step discrete cosine transform（DCT）-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning（DL） method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio（SNR）.The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.