Super resolution reconstruction for medical image based on adaptive multi-dictionary learning and structural self-similarity

Computer Assisted Surgery

Jia Wen¹  Jun Wu¹  Lei Geng¹  Fang Zhang¹  Zhitao Xiao¹  Wen Wang¹  Ping Liu²  Yue Wu² 

[1] Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems;Tianjin Polytechnic University;

关键词: Super-resolution reconstruction;    medical image;    improved adaptive multi-dictionary learning;    non-local structural similarity;   

DOI  :  10.1080/24699322.2018.1557906

来源: DOAJ

【 摘 要 】

To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. In training dictionary section, it uses the upper layer images of pyramid which are generated by the self-similarity of low resolution images. In reconstruction section, the top layer image of pyramid is taken as the initial reconstruction image, and medical image’s SR reconstruction is achieved by regularization term which is the non-local structure self-similarity of the image. This method can make full use of the same scale and different scale similar information of medical images. Simulation experiments are carried out on natural images and medical images, and the experimental results show the proposed method is effective for improving the effect of medical image SR reconstruction.

【 授权许可】

Unknown