Skip to main content

Advertisement

SpringerLink
Log in

De-noising and Demosaicking of Bayer image using deep convolutional attention residual learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Nowadays, the resolution of image sensors in digital cameras is increased by minimizing the size of pixel sensors. As the size is reduced, the pixel sensor receives low light energy and becomes sensitive to thermal noise. The Color Filter Array (CFA) has a significant effect with the presence of noise, and the missing data is required to be reconstructed from the noisy data. This paper proposed a deep convolutional neural network with Honey Badger Algorithm (DCNN-HBA) for Bayer image de-noising. The deep CNN model is easily adopted and flexible for any CFA design with spatially varying color and exposures. After de-noising, attention-based deep residual learning (A-DRL) is applied to de-mosaicking the noise-free Bayer image. The channel attention is involved in which the network considers more relevant information and features. The proposed algorithm improves the quality of the image after reconstruction. The performance of the proposed work is evaluated with the performance metrics such as Peak Signal to Noise Ratio (PSNR), Color Peak Signal to Noise Ratio (CPSNR), Structural Similarity (SSIM), and Mean Structural Similarity (MSSIM) and compared with the traditional de-mosaicking approaches. By using our proposed work, the performance of PSNR, SSIM, CPSNR and MSSIM is improved by 43.23 dB, 0.997, 43.30 dB and 0.9975, 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
Algorithm 1:
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and materials

No data Availability.

References

  1. Arbuj A, Jagtap S, Agrawal A, Dalpe R, Sawant P Deep Joint Denoising and Demosaicking Using Convolutional Neural Network

  2. Buades A, Duran J (2018) Joint de-noising and demosaicking of raw video sequences. In 2018 25th IEEE international conference on image processing (ICIP) IEEE 2172-2176

  3. Dong N, Maggioni M, Yang Y, Pérez-Pellitero E, Leonardis A and McDonagh S (n.d.) Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images

  4. Elgendy OA, Gnanasambandam A, Chan SH, Ma J (2021) Low-light demosaicking and de-noising for small pixels using learned frequency selection. IEEE Transact Comput Imag 7:137–150

    Article  Google Scholar 

  5. Guo S, Liang Z, Zhang L (2021) Joint de-noising and demosaicking with green channel prior for real-world burst images. IEEE Trans Imag Proc 30:6930–6942

    Article  Google Scholar 

  6. Janjušević N, Khalilian-Gourtani A, Wang Y (2021) CDLNet: noise-adaptive convolutional dictionary learning network for blind Denoising and Demosaicing. arXiv preprint arXiv:2112.00913

  7. Jayachandran S (2017) Digital imaging in dentistry: a review. Contemporary Clin Dentis 8(2):193–194

    Article  Google Scholar 

  8. Jin Q, Facciolo G, Morel JM (2020) A review of an old dilemma: Demosaicking first, or de-noising first? In proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops 514-515.

  9. John Peter K, Prabhu S (2017) Demosaicking on color filter array images using improved posterior decision approach. Int J Print Packag Allied Sci 5(1):729–739

    Google Scholar 

  10. Khadidos AO, Khadidos AO, Khan FQ, Tsaramirsis G, Ahmad A (2021) Bayer image demosaicking and de-noising based on specialized networks using deep learning. Multimed Syst, Springer 27(4):807–819

    Article  Google Scholar 

  11. Kiku D, Monno Y, Tanaka M, Okutomi M (2016) Beyond color difference: residual interpolation for color image demosaicking. IEEE Trans Imag Proc 25(3):1288–1300

    MathSciNet  MATH  Google Scholar 

  12. Kim Y, Ryu H, Lee S, Lee YJ (2020) Joint Demosaicing and Denoising based on Interchannel nonlocal mean weighted moving least squares method. Sensors 20(17):4697

    Article  Google Scholar 

  13. Kokkinos F, Lefkimmiatis S (2018) Deep image demosaicking using a cascade of convolutional residual de-noising networks. In proceedings of the European conference on computer vision (ECCV) 303-319

  14. Ma K, Gharbi M, Adams A, Kamil S, Li TM, Barnes C, Ragan-Kelley J (2022) Searching for fast Demosaicking algorithms. ACM Transac Graph (TOG) 41:1–18

    Google Scholar 

  15. Mihoubi S, Lapray PJ, Bigué L (2018) Survey of demosaicking methods for polarization filter array images. Sensors 18(11):3688

    Article  Google Scholar 

  16. Morrison AO, Gardner JM (2015) Microscopic image photography techniques of the past, present, and future. Archives Pathol Lab Med 139(12):1558–1564

    Article  Google Scholar 

  17. Ni Z, Ma KK, Zeng H, Zhong B (2020) Color image demosaicing using progressive collaborative representation. IEEE Trans Imag Proc 29:4952–4964

    Article  MATH  Google Scholar 

  18. Park SH, Kim HS, Lansel S, Parmar M, Wandell BA (2009) A case for de-noising before demosaicking color filter array data. In 2009 Conference record of the forty-third Asilomar conference on signals, Systems and Computers, IEEE 860–864

  19. Qiao Z, Wen X, Zhou X, Qin F, Liu S, Gao B, Liu W, Chi D, Liu Z (2023) Adaptive iterative guided filtering for suppressing background noise in ptychographical imaging. Optics LasEngin, Elsevier 160:107233

    Google Scholar 

  20. Rafi Nazari M (2017) De-noising and demosaicking of color images. PhD diss., Université d'Ottawa/University of Ottawa

  21. Rostami M, Berahmand K, Nasiri E, Forouzandeh S (2021) Review of swarm intelligence-based feature selection methods. Engineering Applications of Artificial Intelligence, Elsevier, 100: 104210

  22. Syu NS, Chen YS, Chuang YY (2018) Learning deep convolutional networks for demosaicing. arXiv preprint arXiv:1802.03769

  23. Tan DS, Chen WY, Hua KL (2018) DeepDemosaicking: adaptive image demosaicking via multiple deep fully convolutional networks. IEEE Trans Imag Proc 27(5):2408–2419

    Article  MathSciNet  MATH  Google Scholar 

  24. Tan H, Xiao H, Liu Y, Zhang M (2022) Two-stage CNN model for joint Demosaicing and Denoising of burst Bayer images. Computational intelligence and neuroscience, hindawi.com, 2022

  25. Tian C, Xu Y, Zuo W (2020) Image de-noising using deep CNN with batch renormalization. Neural Netw, Elsevier 121:461–473

    Article  Google Scholar 

  26. Wang Y, Cao R, Guan Y, Liu T, Yu Z (2021) A deep survey in the applications of demosaicking. In 2021 3rd international academic exchange conference on science and technology innovation (IAECST), IEEE 596-602.

  27. Wang S, Zhao M, Dou R, Yu S, Liu L, Wu N (2021) A compact high-quality image Demosaicking neural network for edge-computing devices. Sensors 21(9):3265

    Article  Google Scholar 

  28. Wu F, Huang T, Dong W, Shi G, Zheng Z, Li X (2021) Toward blind joint demosaicing and de-noising of raw color filter array data. Neurocomput Elsevier 453:369–382

    Article  Google Scholar 

  29. Ye W, Ma KK (2015) Color image demosaicing using iterative residual interpolation. IEEE Trans Imag Proc 24(12):5879–5891

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhang C, Li Y, Wang J, Hao P (2016) Universal demosaicking of color filter arrays. IEEE Trans Imag Proc 25(11):5173–5186

    Article  MathSciNet  MATH  Google Scholar 

Download references

Funding

No funding is provided for the preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Viswajyothi College of Engineering and Technology, Muvattupuzha, Ernakulam, India

    S.P. Predeep Kumar

  2. Department of Computer Science and Engineering, Mangalam College of Engineering, Ettumanoor, Kottayam, India

    K. John Peter & C. Sahaya Kingsly

Authors
  1. S.P. Predeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. John Peter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Sahaya Kingsly
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have equal contributions to this work.

Corresponding author

Correspondence to S.P. Predeep Kumar.

Ethics declarations

Conflict of interest

Authors S.P. Predeep Kumar, K. John Peter, C. Sahaya Kingsly declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any authors.

Consent to participate

All the authors involved have agreed to participate in this submitted article.

Consent to publish

All the authors involved in this manuscript give full consent for publication of this submitted article.

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

Kumar, S.P., Peter, K.J. & Kingsly, C.S. De-noising and Demosaicking of Bayer image using deep convolutional attention residual learning. Multimed Tools Appl 82, 20323–20342 (2023). https://doi.org/10.1007/s11042-023-14334-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14334-z

Key words

Search

Navigation