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Super-resolution based on self-example learning and guided filtering

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Abstract

A sparse representation (using self-example dictionary learning)-based framework for denoising and super-resolution (SR) is proposed. The proposed scheme makes use of fast nonnegative orthogonal matching pursuit for the sparse coding. The dictionary learning is implemented using the K-singular value decomposition. The scheme preprocesses the low-resolution noisy image with a denoising algorithm. The SR versions of the noisy and denoised images are computed by self-example learning algorithm. The resultant SR images are combined through guided (edge preserving and scale aware) filtering technique that preserves high-frequency textural information to obtain a final SR image. Quantitative analysis and visual results demonstrate the significance of proposed scheme.

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Authors and Affiliations

  1. National University of Sciences and Technology, Islamabad, Pakistan

    Alina Majeed Chaudhry & Abdul Ghafoor

  2. COMSATS University, Islamabad, Pakistan

    M Mohsin Riaz

Authors
  1. Alina Majeed Chaudhry
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  2. M Mohsin Riaz
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  3. Abdul Ghafoor
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Correspondence to Abdul Ghafoor.

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Chaudhry, A.M., Riaz, M.M. & Ghafoor, A. Super-resolution based on self-example learning and guided filtering. SIViP 13, 237–244 (2019). https://doi.org/10.1007/s11760-018-1350-5

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  • DOI: https://doi.org/10.1007/s11760-018-1350-5

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