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CNN framework for optical image super-resolution and fusion

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Abstract

This paper presents an algorithm for image super-resolution based on CNN. The objective is to work on medical images including images with tumors for better diagnosis. The CNN is utilized to upscale the images. Three steps are incorporated in the proposed algorithm including the patch extraction and representation which used to extract patches from low-resolution images by pretrained filters, and then nonlinear mapping is used to map nonlinearly each high-dimensional vector. The high-resolution image is obtained in the reconstruction process. The obtained results are evaluated with metrics including PSNR, and the average PSNR for CT is 29.755 and 29.12 for MRI. Results reveal high quality of the obtained images. In addition, a fusion-based step is implemented on super-resolution MRI with CT images using CNNs in fusion, we found that SRCNN gives good results than Bi-cubic interpolation. The comparison for the fusion results with and without super-resolution, SRCNN before fusion improves the results.

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Acknowledgements

The authors are very grateful to all the institutions given in the affiliation list for performing this research work successfully. The authors would like to thank Prince Sultan University for their support.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Shibīn al-Kawm, Egypt

    Walid El-Shafai, Randa Aly, Taha E. Taha & Fathi E. Abd El-Samie

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  1. Walid El-Shafai
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  2. Randa Aly
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  3. Taha E. Taha
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  4. Fathi E. Abd El-Samie
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Correspondence to Walid El-Shafai.

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El-Shafai, W., Aly, R., Taha, T.E. et al. CNN framework for optical image super-resolution and fusion. J Opt 53, 797–816 (2024). https://doi.org/10.1007/s12596-023-01122-z

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