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Kernel estimation and optimization for image de-blurring using mask construction and super-resolution

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Abstract

The blur of image is displayed by convolving an image with the blur kernel. Thus, estimating blur kernel is significants of image de-blurring. We aim at obtaining optimized blur kernel of image for de-blurring. Kernel estimation and optimization for de-blurring of image is proposed in this paper. Mask is created for kernel estimation through super pixels and gradient map (generated through illuminant layer). Structural information is extracted through creation of mask through super-pixels, instead of using exemplars and together with the illuminant part of image and gradient map estimates the kernel which is optimized using super-resolution. The proposed method extracts good structural information and edges, hence better de-blurring as compared to state-of-art de-blurring methods.

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Mehwish Iqbal, Abdul Ghafoor & Attiq Ahmad

  2. Centre for Advanced Studies in Telecommunication (CAST), COMSATS, Islamabad, Pakistan

    Muhammad Mohsin Riaz

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  1. Mehwish Iqbal
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  2. Muhammad Mohsin Riaz
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  3. Abdul Ghafoor
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  4. Attiq Ahmad
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Correspondence to Abdul Ghafoor.

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Iqbal, M., Riaz, M.M., Ghafoor, A. et al. Kernel estimation and optimization for image de-blurring using mask construction and super-resolution. Multimed Tools Appl 80, 10361–10372 (2021). https://doi.org/10.1007/s11042-020-09762-0

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  • DOI: https://doi.org/10.1007/s11042-020-09762-0

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