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Adaptive fractional masks and super resolution based approach for image enhancement

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Abstract

This paper presents an image enhancement technique based on super-resolution approach. The method uses fractional filters and reconstructs the output image by projection on convex sets (POCS) method. First, we generate a reference frame by using low-resolution frames and enhanced it by an adaptive fractional mask. Then the speed-up robust feature (SURF) is used to find the matching between low-resolution frames and the reference frame. Finally, the residuals between matching are reduced by the POCS reconstruction approach. To recover the high-frequency components, we have used a fractional integral mask in the POCS reconstruction process. We have compared the experimental results with some other existing methods from literature. Simulation results show that the proposed approach is efficient and returns a good quality image.

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Notes

  1. In the experiments, we have taken the value of parameter p as 1.01.

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Acknowledgments

The authors sincerely thank the Editor and reviewers for their constructive comments to improve the quality of the manuscript.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Indian Institute of Technology (BHU), Varanasi, 221005, India

    Anil K. Shukla, Rajesh K. Pandey & Swati Yadav

  2. Center for Advanced Biomaterials and Tissue Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India

    Rajesh K. Pandey

Authors
  1. Anil K. Shukla
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  2. Rajesh K. Pandey
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  3. Swati Yadav
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Correspondence to Rajesh K. Pandey.

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Appendix

Appendix

1.1 A Performance of the proposed algorithm with different filters

To show the effectiveness of the proposed fractional masks in the designed algorithm, we have presented the experimental results in Table 7 and Fig. 12. The results are obtained by replacing the discussed fractional filters with different filters, such as average filter, median filter, and the Gaussian smoothing filter in the proposed algorithm.

Table 7 The objective quality assessment of proposed algorithm with different filters
Full size table
Fig. 12
figure 12

Enhancement results of the proposed algorithm with different filters: First row presents the result obtained by with average filter; Second row presents the results obtained with median filter; Third row presents the results obtained with the Gaussian smoothing filter; Fourth row presents the results obtained with the proposed fractional filters

Full size image

In the proposed super-resolution based algorithm, fractional filters are part of the designed algorithm. However, the developed algorithm can work with other known filters also. To show the importance of the designed fractional masks in the introduced algorithm, we have depicted the results with different filters in Table 7. It is observed that the proposed algorithm gives better results in terms of entropy and contrast when it is applied with introduced fractional masks. The SSIM of the proposed algorithm is higher when it is used with average, median, and the Gaussian smoothing filters. However, the proposed algorithm, when it is applied with designed fractional masks, returns the high-quality enhanced image (Fig. 12). The image obtained by the proposed algorithm, when it is used with other filters such as average, median, and the Gaussian smoothing, is faded and returns the low values of entropy and the contrast ratio in comparison with introduced fractional masks. These results show the importance of the discussed fractional masks in the proposed algorithm of super-resolution.

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Shukla, A.K., Pandey, R.K. & Yadav, S. Adaptive fractional masks and super resolution based approach for image enhancement. Multimed Tools Appl 80, 30213–30236 (2021). https://doi.org/10.1007/s11042-020-08968-6

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