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Image super resolution using distributed locality sensitive hashing for manifold learning

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Abstract

In this paper we propose a distributed locality sensitive hashing based framework for image super resolution exploiting computational and storage efficiency of cloud. Now days huge multimedia data is available on the cloud which can be utilized using store anywhere and excess anywhere model. It may be noted that super resolution is required for consumer electronics display devices due to various reasons. The propose framework exploits the image correlation for image super resolution using locality sensitive hashing (LSH) for manifold learning. In our work we have exploited the benefits of manifold learning for image super resolution, which in-turn is a highly time complex operation. The time complexity is involved due to finding the approximate nearest neighbors from trillion of image patches for locally linear embedding (LLE) operation. In our approach it is mitigated by using a distributed framework which internally uses hash tables for mapping of patches in the target image from a database of internet picture collection. The proposed framework for super resolution provides promising results in comparison to existing approaches.

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Notes

  1. https://github.com/JalaliLabUCLA/Jalali-Lab-Implementation-of-RAISR

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Author notes

  1. Santanu Chaudhury

    Present address: Indian Institute of Technology, Jodhpur, India

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, Delhi, India

    Anurag Tripathi, Santanu Chaudhury & Arun Singh

  2. E.C.E. Department, Jaypee Institute of Information Technology, Noida, India

    Abhinav Gupta

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  1. Anurag Tripathi
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  2. Abhinav Gupta
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Correspondence to Abhinav Gupta.

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Tripathi, A., Gupta, A., Chaudhury, S. et al. Image super resolution using distributed locality sensitive hashing for manifold learning. Multimed Tools Appl 78, 25673–25684 (2019). https://doi.org/10.1007/s11042-019-07799-4

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