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Super-resolution image reconstruction for mobile devices

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Abstract

With advanced mobile devices, the mobile applications of the high-definition display attract a lot of attentions nowadays. The existing image super-resolution methods are computationally inefficient for the high-definition display on the mobile devices. In this paper, we point out that the above critical issue deteriorates the display quality of the high-definition mobile devices. We propose an efficient and effective algorithm to reconstruct the high-resolution images for the mobile devices. Our algorithm outperforms previous approaches in not only smaller running time but also the higher quality of the super-resolution image reconstruction for the mobile devices.

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Notes

  1. An app is software running on a mobile device such as a mobile phone, PDAs or MP3 players performing specific tasks typically restricted to desktop or notebook computers. These apps are either pre-installed on the mobile devices during manufacture, or downloaded by users from Apple stores, android market, or other mobile software distribution platforms.

  2. The noise is random additive noise. Gaussian white noise is a special case of the random additive noise. In most cases, since the subsampling is generally much more significant than the noise, we can adopt the Gaussian white noise to model the noise for simplicity [40].

  3. Affine flow approximates the motion of the surface as an affine transformation.

  4. The regularization parameter λ controls the degree of smoothness (i.e., degree of bias) of the solution, and is usually small. Analytical methods for choosing an optimal parameter λ are discussed in [66].

  5. Hadamard [28] defined ill-posed problems whose solution does not exist or it is not unique or it is not stable under perturbations on data. It was with the intent of saving mathematicians and computational scientists substantial time and trouble.

  6. In fact, the inverse of the block-Toeplitz matrix H almost exists [77].

  7. In this paper, the accuracy is defined as the number of the sequences under successfully estimated parameters over the total number of testing sequences.

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Acknowledgments

The work was supported in part by the National Science Council of Taiwan, ROC, under Contracts NSC-101-2221-E-025-011.

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  1. Department of Multimedia Design, National Taichung University of Science and Technology, Taichung, Taiwan, ROC

    Chung-Hua Chu

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Communicated by C. Xu.

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Chu, CH. Super-resolution image reconstruction for mobile devices. Multimedia Systems 19, 315–337 (2013). https://doi.org/10.1007/s00530-012-0276-y

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