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HighBoostNet: a deep light-weight image super-resolution network using high-boost residual blocks

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Abstract

Image distortion is an inevitable part of the image acquisition process, which negatively affects the high-frequency contents of the images. Therefore, it is important to improve the high-frequency contents of the acquired degraded images in the imaging systems and devices. High-boost filtering is an effective method that is used by the scanning and printing devices for enhancing the high-frequency contents of the images and improving their visual quality. In view of this, in this paper, we first develop a residual block for the task of image super-resolution that employs the high-boost filtering operations. It is demonstrated that the super-resolution network, which is formed by a cascade of the proposed residual block, is able to provide a high performance. Further, in this paper, we propose a novel learning method that improves the generalization capability of our deep super-resolution network. Specifically, we generalize the mapping between the spaces of the degraded low-resolution image and the ground-truth image by employing the multiple supervised learning strategy. It is shown that the proposed multiple supervised learning strategy leads to obtaining the weights of our super-resolution network in such a way that its performance is still high when the images are degraded by a set of degradation parameters that is slightly different than that used for the training process.

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The datasets analyzed during the current study are referred in the paper.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Alireza Esmaeilzehi & Lei Ma

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

    M. N. S. Swamy & M. Omair Ahmad

Authors
  1. Alireza Esmaeilzehi
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  2. Lei Ma
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  3. M. N. S. Swamy
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  4. M. Omair Ahmad
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Correspondence to Alireza Esmaeilzehi.

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Esmaeilzehi, A., Ma, L., Swamy, M.N.S. et al. HighBoostNet: a deep light-weight image super-resolution network using high-boost residual blocks. Vis Comput 40, 1111–1129 (2024). https://doi.org/10.1007/s00371-023-02835-9

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