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Image super resolution boosting using beta wavelet

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Abstract

Image super resolution (SR) is a critical category within the field of image processing techniques that aims to improve the resolution of both images and videos. Its significance is especially pronounced in image processing and computer vision domain. In recent years, deep learning approaches for image super resolution have consistently achieved remarkable advancements. This paper presents a new approach for enhancing the quality of images. More precisely, we have contributed in the single image super-resolution field by exploiting beta wavelet in deep convolutional neural network (DCNN), which we called deep convolutional neural network for image super resolution boosting using beta wavelet (ISR2BW). First, the low-resolution image undergoes a division into four sub-bands, consisting of one approximation and three distinct detail components. Second, various analytic beta wavelet (ABW) filters are computed to forecast and restore the missing details within the approximation sub-band. Then, this output is used as input to the DCNN in order to improve SR wavelet coefficients. The predicted approximation and three sub-bands are subsequently subjected to the inverse 2D discrete wavelet transformation to generate the high image super resolution. The proposed method is assessed through extensive experiments by examining the structural similarity index for measure, the peak signal to noise ratio, and the mean squared error indicators. The experimental results highlight the ISR2BW method's effectiveness and efficiency when compared to other state-of-the-art methods on commonly used benchmarking datasets.

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Availability of data and materials

The datasets used to evaluate the performance of our method are publicly available: 91 images from Yang et al. [17], Set5 [50], Set14 [51] and B100 [23].

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Acknowledgements

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

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Authors and Affiliations

  1. National Engineering School of Gabès (ENIG), University of Gabès, Gabès, Tunisia

    Nesrine Chaibi & Mourad Zaied

  2. Research Team in Intelligent Machines (RTIM), University of Gabès, Gabès, Tunisia

    Nesrine Chaibi, Asma Eladel & Mourad Zaied

  3. Department of Information Technology, College of Computer Science and Information Technology, Al-Baha University, Al Baha, Saudi Arabia

    Najib Ben Aoun

  4. REGIM-Lab: Research Groups in Intelligent Machines, National School of Engineers of Sfax (ENIS), University of Sfax, Sfax, Tunisia

    Najib Ben Aoun

  5. Higher Institute of Computing and Multimedia of Gabès, University of Gabès, Gabès, Tunisia

    Asma Eladel

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  1. Nesrine Chaibi
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Contributions

NC, has proposed the paper idea. She wrote the paper and performed all the experiments in the proposed manuscript. NBA has helped in the methodology design, revised the whole manuscript and validated the work experimentations. AEA has revised the whole manuscript, and she approved the paper’s content and figures. MZ has supervised the work and suggested a valuable journal as yours (SIVP). He was very helpful and significant. Furthermore, he was the creator of Beta Wavelet Network that has been used in this work.

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Correspondence to Nesrine Chaibi.

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Chaibi, N., Ben Aoun, N., Eladel, A. et al. Image super resolution boosting using beta wavelet. SIViP 18, 1821–1831 (2024). https://doi.org/10.1007/s11760-023-02887-3

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