Laser & Optoelectronics Progress, Volume. 55, Issue 5, 051009(2018)
An Improved Single-Frame Super-Resolution Algorithm for Magnetic Resonance Image
Medical image processing is an important and key problem in image processing. High-resolution images with abundant details contribute to assisting physicians and computer aided diagnosis programs. According to the characteristics of magnetic resonance images, we propose a single-frame super-resolution reconstruction method based on wavelet features and clustered dictionaries. In the training phase, the multiscale wavelet features of low-resolution images and all high-frequency components of high-resolution images are extracted, and all of these feature images are overlapping and separated into patches. Then, K-means algorithm is used to cluster feature patches into several classes, for each class a pair of dictionaries is learned using K-singular value decomposition. In the reconstruction phase, each low-resolution patch is classified and sparsely represented with its corresponding dictionary atoms. Iterative back projection is used for post-processing to further improve the reconstruction quality. Experimental results show that the proposed method outperforms other main-stream methods, both visually and quantitatively.
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Jinghui Chu, Fengshuo Hu, Jiaqi Zhang, Wei Lü. An Improved Single-Frame Super-Resolution Algorithm for Magnetic Resonance Image[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051009
Category: Image processing
Received: Oct. 18, 2017
Accepted: --
Published Online: Sep. 11, 2018
The Author Email: Lü Wei ( luwei@tju.edu.cn)