A Survey -- Super Resolution Techniques for Multiple, Single, and Stereo Images

Balure, Chandra Shaker; Kini, M. Ramesh

doi:10.1109/ised.2014.53

Cited by 1 publication

(1 citation statement)

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“…However, such techniques estimate new points assuming the homogeneity of regions, hence these interpolated images are typically blurred. Among different techniques [4], [5], example-based methods have been mostly proposed in recent state of the art works, either by exploiting internal similarities of the same image [6], [7], or learning from external training datasets [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks

Thurnhofer‐Hemsi

López‐Rubio

Roé-Vellvé

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Enhancing resolution is a permanent goal in magnetic resonance (MR) imaging, in order to keep improving diagnostic capability and registration methods. Super-resolution (SR) techniques are applied at the postprocessing stage, and their use and development have progressively increased during the last years. In particular, example-based methods have been mostly proposed in recent state-of-the-art works. In this paper, a combination of a deep-learning SR system and a random shifting technique to improve the quality of MR images is proposed, implemented and tested. The model was compared to four competitors: cubic spline interpolation, non-local means upsampling, low-rank total variation and a three-dimensional convolutional neural network trained with patches of HR brain images (SRCNN3D). The newly proposed method showed better results in Peak Signal-to-Noise Ratio, Structural Similarity index, and Bhattacharrya coefficient. Computation times were at the same level as those of these up-to-date methods. When applied to downsampled MR structural T1 images, the new method also yielded better qualitative results, both in the restored images and in the images of residuals.

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