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Single Noisy Image Super Resolution by Minimizing Nuclear Norm in Virtual Sparse Domain

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Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 841))

Abstract

Super-resolving a noisy image is a challenging problem, and needs special care as compared to the conventional super resolution approaches, when the power of noise is unknown. In this scenario, we propose an approach to super-resolve single noisy image by minimizing nuclear norm in a virtual sparse domain that tunes with the power of noise via parameter learning. The approach minimizes nuclear norm to explore the inherent low-rank structure of visual data, and is further augmented with coarse-to-fine information by adaptively re-aligning the data along the principal components of a dictionary in virtual sparse domain. The experimental results demonstrate the robustness of our approach across different powers of noise.

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Notes

  1. 1.

    If we increase the area of the region to find similarity, a better result is expected in the cost of increased computational burden.

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Author information

Authors and Affiliations

  1. Image Processing and Computer Vision Lab, Department of Electrical Engineering, IIT Madras, Chennai, 600036, India

    Srimanta Mandal & A. N. Rajagopalan

Authors
  1. Srimanta Mandal
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  2. A. N. Rajagopalan
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Correspondence to Srimanta Mandal .

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Editors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Renu Rameshan

  2. Indraprastha Institute of Information Technology, New Delhi, India

    Chetan Arora

  3. Indian Institute of Technology, New Delhi, India

    Sumantra Dutta Roy

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Mandal, S., Rajagopalan, A.N. (2018). Single Noisy Image Super Resolution by Minimizing Nuclear Norm in Virtual Sparse Domain. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_15

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  • DOI: https://doi.org/10.1007/978-981-13-0020-2_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0019-6

  • Online ISBN: 978-981-13-0020-2

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