基于双字典和稀疏表示的医学图像超分辨率重建

在医学影像图像处理过程中,由于成像技术和成像时间的限制,还无法获取满足诊断需求的清晰图像,这使得在现有技术和极短时间内所获取的医学病理图像需要进行超分辨率的重建处理；基于学习的图像超分辨率思想是从已建立的先验模型中重建出高频细节；在文章中,将要估计的高频信息认为是由主要高频和冗余高频两部分组成,提出了一种基于双字典学习和稀疏表示的医学图像超分辨率重建算法,由主要字典学习和冗余字典学习组成,分别渐近地恢复出主要高频细节和冗余高频细节；实验结果的数据分析和视觉效果显示,所提出双层递进方法能够恢复更多的图像细节且在性能指标上比现有的其他几种方法均有所提高。

Super-Resolution Reconstruction of Medical Image Based on Dual-Dictionary and Sparse Representation

Medical diagnosis needs a lot of medical image processing, due to the limitations of imaging technology and imaging time, the medical diagnosis is not able to get the clear image, which is necessary to reconstruct the medical image that have been acquired in the existing technology and considerably short time with super-resolution methods. Example-Based image super-resolution is to reconstruct the high-frequency (HF) details of the image from the prior model. HF will be estimated is considered as a combination of two components:main high-frequency(MHF) and residual high-frequency (RHF) ,this paper proposed a medical image super-resolution using dual-dictionary learning and sparse representation, which makes of the main dictionary and the residual dictionary learning recovering the MHF and RHF, respectively. Experimental results on test image show that by performing the proposed two-layer progressive method, more image details can be recovered and much better results can be achieved than that of existing methods.