A Sub-Dictionary Approach for Image Super Resolution Based on Sparse Representation
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2018-04-18 https://doi.org/10.14419/ijet.v7i2.20.14776 -
Super RESOLUTION (SR), Low-Resolution (LR), High Resolution (HR). -
Abstract
In this paper we attempt to speak to a novel way to deal with single-picture super-determination, established on inadequate flag representation. Investigation on picture estimations prescribes that picture covers can be very much portrayed as a scanty straight amalgamation of basics from a reasonably favored over entire word reference. Roused by this examination, we seek after a meager show for each fix of the low-determination input, and earlier utilize the estimations of this exhibit to induce the high-determination profitability. Theoretic results from compacted recognizing prescribe that under gentle conditions, the meager show can be fittingly enhanced from the down tested signals.We utilize nearby sub-word references to adaptively code picture covers, which can represent picture neighborhood gatherings improved and affirm the sparsity belonging of the picture. Moreover, we rehearse portion weakening to describe HR and LR coding amounts to internment and guide the crucial non-direct association among them. Such speaking to is of focal noticeable quality in the picture SR risky, for the reason that high-arrange estimations assume a considerable part in the modifying of the detail setup of a HR picture.
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References
[1] Ahmed J & Klette R, “Coupled multiple dictionary learning based on edge sharpness for single-image super-resolutionâ€, 23rd International Conference on Pattern Recognition (ICPR), (2016), pp.3838-3843.
[2] Jing XY, Zhu X, Wu F, Hu R &You X, et.al, “Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learningâ€, IEEE transactions on image processing, Vol.26, No.3, (2017), pp.1363-1378.
[3] Yeganli F, Nazzal M & Ozkaramanli H, “Super-resolution using multiple structured dictionaries based on the gradient operator and bicubic interpolationâ€, 24th Signal Processing and Communication Application Conference (SIU), (2016), pp.941-944.
[4] Yang J, Wang Z, Lin Z, Cohen S & Huang T, “Coupled dictionary training for image super-resolutionâ€, IEEE transactions on image processing, Vol.21, No.8, (2012), pp.3467-3478.
[5] Li Y, Huang JB, Ahuja N & Yang MH, “Deep joint image filteringâ€, European Conference on Computer Vision,(2016), pp. 154-169.
[6] Song P, Deng X, Mota JF, Deligiannis N, Dragotti PL & Rodrigues MR, “Multimodal Image Super-Resolution via Joint Sparse Representations induced by Coupled Dictionariesâ€, arXiv preprint arXiv:1709.08680, (2017).
[7] Dong W, Zhang L, Lukac R &Shi G, “Sparse representation based image interpolation with nonlocal autoregressive modelingâ€, IEEE Trans.Image Process., Vol.22, No.4, (2013), pp.1382–1394.
[8] Gao X, Zhang K, Tao D &Li X, “Joint learning for single-image super-resolution via a coupled constraintâ€, IEEE Trans. Image Process, Vol.21, No.2, (2012), pp.469–480.
[9] Zhang K, Tao D, Gao X, Li X &Xiong Z, “Learning multiple linear mappings for efficient single image super-resolutionâ€, IEEE Trans. Image Process., Vol.24, No.3, (2015), pp.846–861.
[10] Walha R, Drira F, Lebourgeois F, Garcia C &Alimi AM, “Resolution enhancement of textual images via multiple coupled dictionaries and adaptive sparse representation selectionâ€, Int. J. Document Anal. Recognit. (IJDAR), Vol.18, No.1, (2015), pp.87–107.
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How to Cite
Venkatesh, G., & E. Sreenivasa Murthy, K. (2018). A Sub-Dictionary Approach for Image Super Resolution Based on Sparse Representation. International Journal of Engineering & Technology, 7(2.20), 272-275. https://doi.org/10.14419/ijet.v7i2.20.14776Received date: 2018-06-29
Accepted date: 2018-06-29
Published date: 2018-04-18