Skip to main content
SpringerLink
Log in

Image enhancement and blur pixel identification with optimization-enabled deep learning for image restoration

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Image enhancement is the process of enhancing specific aspects of an image, such as its borders or contrast. The procedure of restoring a destroyed image is known as image restoration. A multitude of factors, such as low camera resolution, motion blur, noise, and others, can cause images to degrade throughout the acquisition process. Although image restoration techniques can remove haze from a degraded image, they are problematic for use in a real-time system since they necessitate numerous photographs from the same location. The suggested fractional Jaya Bat algorithm (FJBA) provides picture enhancement and blur pixel identification to address this issue. Firstly, the blur pixel identification is done using a deep residual network (DRN) trained with FJBA considering blurry image. FJBA is created by combining the Jaya Bat algorithm (JBA) and fractional notion (FC). Furthermore, a blurred image is deblurred using a fusion convolutional neural network (CNN) approach tuned through Pelican hunter optimization (PHO). PHO stands for Pelican optimization (PO) and hunter prey optimization (HPO). Lastly, the image is enhanced using the neural fuzzy system (NFS) and the image enhancement conditional generative adversarial network (IE-CGAN), which has been fine-tuned using FJBA. The proposed FJBA-NFS-IE-CGAN provided enhanced performance with the highest PSNR of 50.536 dB, SDME of 60.724 dB, and SSIM of 0.963, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Statlog (Landsat satellite) dataset “https://archive.ics.uci.edu/ml/datasets/Statlog+%28Landsat+Satellite%29” is assessed on March, 2023.

References

  1. Campisi, P., Egiazarian, K. (eds.): Blind image deconvolution: theory and applications. CRC Press, Boca Raton (2017)

    Google Scholar 

  2. Yang, H., Su, X., Chen, S., Zhu, W., Ju, C.: Efficient learning-based blur removal method based on sparse optimization for image restoration. PLoS ONE 15(3), e0230619 (2020)

    Article  Google Scholar 

  3. Trouvé, P., Champagnat, F., Le Besnerais, G., Idier, J.: Single image local blur identification. In: 2011 18th IEEE International Conference on Image Processing, pp. 613–616. IEEE (2011)

  4. Jezierska, A., Talbot, H., Pesquet, J.C.: Spatially variant psf modeling in confocal macroscopy. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 489–492. IEEE (2018)

  5. Tezaur, R., Kamata, T., Hong, L., Slonaker, S.S.: A system for estimating optics blur psfs from test chart images. In: Digital Photography XI, vol. 9404, pp. 84–93. SPIE (2015)

  6. Huang, Y., Chouzenoux, E., Elvira, V.: Probabilistic modeling and inference for sequential space-varying blur identification. IEEE Trans. Comput. Imaging 7, 531–546 (2021)

    Article  MathSciNet  Google Scholar 

  7. Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., Shao, L.: Learning enriched features for fast image restoration and enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1934–1948 (2022)

    Article  Google Scholar 

  8. Maini, R., Aggarwal, H.: A comprehensive review of image enhancement techniques. arXiv preprint arXiv:1003.4053 (2010)

  9. Agaian, S.S., Panetta, K., Grigoryan, A.M.: A new measure of image enhancement. In: IASTED International Conference on Signal Processing & Communication, pp. 19–22 (2000)

  10. Singh, G., Mittal, A.: Various image enhancement techniques—a critical review. Int. J. Innov. Sci. Res. 10(2), 267–274 (2014)

    Google Scholar 

  11. Sun, X., Zheng, L.: Dissecting person re-identification from the viewpoint of viewpoint. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 608–617 (2019)

  12. Pratt, S.G., Bell, J.L.: Analytical observational study of nonfatal motor vehicle collisions and incidents in a light-vehicle sales and service fleet. Accid. Anal. Prev. 129, 126–135 (2019)

    Article  Google Scholar 

  13. Shen, L., Yue, Z., Feng, F., Chen, Q., Liu, S., Ma, J.: Msr-net: Low-light image enhancement using deep convolutional network. arXiv preprint arXiv:1711.02488 (2017)

  14. Li, G., Yang, Y., Qu, X., Cao, D., Li, K.: A deep learning based image enhancement approach for autonomous driving at night. Knowl. Based Syst. 213, 106617 (2021)

    Article  Google Scholar 

  15. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 43(7), 2480–2495 (2020)

    Article  Google Scholar 

  16. Chang, M., Feng, H., Xu, Z., Li, Q.: Low-light image restoration with short-and long-exposure raw pairs. IEEE Trans. Multimed. 24, 702–714 (2021)

    Article  Google Scholar 

  17. Huang, L., Xia, Y.: Joint blur kernel estimation and CNN for blind image restoration. Neurocomputing 396, 324–345 (2020)

    Article  Google Scholar 

  18. Wang, R.: Exploring Wavelet transform-based image enhancement algorithm for image restoration of long march national cultural park. J. Environ. Public Health (2022)

  19. Panetta, K., KM, S.K., Rao, S.P., Agaian, S.S.: Deep perceptual image enhancement network for exposure restoration. IEEE Trans. Cybern. (2022)

  20. Hyperspectral Remote Sensing Scenes dataset https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Cuprite is assessed on June, 2023.

  21. Kaur, A., Sharma, S., Mishra, A.: A novel Jaya-BAT algorithm-based power consumption minimization in cognitive radio network. Wirel. Pers. Commun. 108, 2059–2075 (2019)

    Article  Google Scholar 

  22. Zhao, C., Xue, D., Chen, Y.: A fractional order PID tuning algorithm for a class of fractional order plants. In: IEEE International Conference Mechatronics and Automation, vol. 1, pp. 216–221. IEEE (2005)

  23. Trojovský, P., Dehghani, M.: Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications. Sensors 22(3), 855 (2022)

    Article  Google Scholar 

  24. Naruei, I., Keynia, F., Sabbagh Molahosseini, A.: Hunter–prey optimization: algorithm and applications. Soft. Comput. 26(3), 1279–1314 (2022)

    Article  Google Scholar 

  25. Vieira, J., Dias, F.M., Mota, A.: Neuro-fuzzy systems: a survey. In: 5th WSEAS NNA International Conference on Neural Networks and Applications, Udine, Italia, pp. 1–6 (2004)

  26. Li, R., Pan, J., Li, Z., Tang, J.: Single image dehazing via conditional generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8202–8211 (2018)

  27. Chen, Z., Chen, Y., Wu, L., Cheng, S., Lin, P.: Deep residual network-based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Convers. Manag. 198, 111793 (2019)

    Article  Google Scholar 

  28. Bhaladhare, P.R., Jinwala, D.C.: A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm. Adv. Comput. Eng. (2014)

  29. Rao, R.: Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7(1), 19–34 (2016)

    Google Scholar 

  30. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74 (2010)

  31. Ram Prabhakar, K., Sai Srikar, V., Venkatesh Babu, R.: Deepfuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4714–4722 (2017)

  32. Karaboga, D., Kaya, E.: Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif. Intell. Rev. 52, 2263–2293 (2019)

    Article  Google Scholar 

  33. Kuang, X., Sui, X., Liu, Y., Chen, Q., Gu, G.: Single infrared image enhancement using a deep convolutional neural network. Neurocomputing 332, 119–128 (2019)

    Article  Google Scholar 

  34. YOLO Object Detection dataset, https://www.kaggle.com/code/rahulkumarpatro/yolo-object-detection. Accessed June 2023

  35. Kollem, S., Reddy, K.R., Sreejith, S., Prasad, C.R., Samala, S., Pardhu, T.: A general regression neural network based blurred image restoration. In: The Proceeding of Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT). IEEE, Mandya, India (2022)

  36. Mou, C., Wang, Q., Zhang, J.: Deep generalized unfolding networks for image restoration. In: The Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17399–17410 (2022)

Download references

Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

Funding

This research did not receive any specific funding.

Author information

Authors and Affiliations

  1. Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, 641008, India

    S. P. Premnath

  2. Computer Science and Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamilnadu, 600117, India

    P. Sheela Gowr

  3. Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, 641008, India

    J. P. Ananth & Sajeev Ram Arumugam

  4. Department of Artificial Intelligence and Data Science, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, 641008, India

    Sajeev Ram Arumugam

Authors
  1. S. P. Premnath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Sheela Gowr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. P. Ananth
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sajeev Ram Arumugam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to S. P. Premnath.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

Not applicable.

Informed consent

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Premnath, S.P., Gowr, P.S., Ananth, J.P. et al. Image enhancement and blur pixel identification with optimization-enabled deep learning for image restoration. SIViP (2024). https://doi.org/10.1007/s11760-024-03092-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03092-6

Keywords

Search

Navigation