29 August 2016 Single image super resolution of 3D MRI using local regression and intermodality priors
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100334C (2016); doi: 10.1117/12.2243617
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
Clinical practice requires multiple scans with different modalities for diagnostic tasks, but each scan does not produce the image of the same resolution. Such phenomenon may influence the subsequent analysis such as registration or multimodal segmentation. Therefore, performing super-resolution (SR) on clinical images is needed. In this paper, we present a unified SR framework which takes advantages of two primary SR approaches – self-learning SR and learning-based SR. Through the self-learning SR process, we succeed in obtaining a second-order approximation of the mapping functions between low and high resolution image patches, by leveraging a local regression model and multi-scale self-similarity. Through the learning-based SR process, such patch relations are further refined by using the information from a reference HR image. Extensive experiments on open-access MRI images have validated the effectiveness of the proposed method. Compared to other advanced SR approaches, the proposed method provides more realistic HR images with sharp edges.
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Jing Hu, Xi Wu, Jiliu Zhou, "Single image super resolution of 3D MRI using local regression and intermodality priors", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334C (29 August 2016); doi: 10.1117/12.2243617; http://dx.doi.org/10.1117/12.2243617
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KEYWORDS
Lawrencium

Magnetic resonance imaging

Image resolution

Super resolution

3D image processing

Image processing

Associative arrays

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