Skip to main content
SpringerLink
Log in

An efficient image dahazing using Googlenet based convolution neural networks

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

Abstract

The dehazing is a significant colour image-processing technique for attaining a high quality of images from haze images. Now a day’s digital cameras are playing an important key role in many applications, such as scanning, HD image generation, traffic user, tourists, especially in hilly areas satellite and radar applications. The dehazing is a complex function for digital cameras since it converts a bayers mosaic image into a final color image and then estimates the output image. The full colour image cannot be reconstructed from incomplete samples due to haze problem, and hence appropriate dehazing models are implemented to overcome this problem. In this work, a dehazing algorithm is proposed with GoogleNet deep learning mechanism for getting the improved quality of the image. In this investigation, GoogleNet deep learning model is used to reconstruct the full color image without degrading the sensitivity and resolution. In this proposed work, the deep learning based convolutional networks are realized using demosaicking for pre-processing to reproduce the dehaged full color images from the incomplete samples. In this demosaicking task, the first step is demosaicking to produce a rough image consists of unwanted color artifacts. Second step is the refining step, in which, the deep residual estimation is used to decrese the color artifacts along with the multi model fusion concept to produce good quality output images. The performance measures, viz., Peak-Signal to Noise Ratio (PSNR), Structural similarity Index (SSIM), and Mean Square Error (MSE) are evaluated and compared with existing models. The PSNR is 32.78, SSIM is 0.9412, MSE is 0.098, F1-score is 0.989, sensitivity is 0.972, and CC is 0.978 have been attained by this optimized algorithm. The GoogleNet technology outperforms the existing methods. This deep learning mechanism does process the input hazy images and decomposes the smoothness hazy free elements from texture elements.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Ancuti CO, Ancuti C, Hermans C, Bekaert P (2010) A fast semi-inverse approach to detect and remove the haze from a single image. In: Asian Conference on Computer Vision. Springer, Berlin, pp 501–514

  2. Ancuti C, Ancuti CO, Haber T, Bekaert P (2012) Enhancing underwater images and videos by fusion. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 81–88

  3. Babu GH, Venkatram N (2020) A survey on analysis and implementation of state-of-the-art haze removal techniques. J Vis Commun Image Represent 72:102912

  4. Berman D, Avidan S (2016)Non-local dahazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1674–1682

  5. Berman D, Avidan S (2016)Non-local dahazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1674–1682

  6. Cai B, Xu X, Jia K, Qing C, Tao D (2016) Dehazenet: An end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198

    Article  MathSciNet  MATH  Google Scholar 

  7. Chen C, Do MN, Wang J (2016) Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In: European Conference on Computer Vision. Springer, Cham, pp 576–591

  8. Engin D, Genç A, Kemal Ekenel H (2018) Cycle-dehaze: Enhanced cyclegan for single dahazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 825–833

  9. Fattal R (2014) Dehazing using color-lines. ACM Trans graphics (TOG) 34(1):1–14

  10. He M, SUNJ TXO (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341

    Article  Google Scholar 

  11. He K, Sun J, Tang X (2010) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Google Scholar 

  12. He L, Zhao J, Zheng N, Bi D (2016) Haze removal using the difference-structure-preservation prior. IEEE Trans Image Process 26(3):1063–1075

    Article  MathSciNet  MATH  Google Scholar 

  13. Levin A, Lischinski D, Weiss Y (2007) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242

    Article  Google Scholar 

  14. Li B, Peng X, Wang Z, Xu J, Feng D (2017) Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE international conference on computer vision, pp 4770–4778

  15. Lin Z, Wang X (2012) Dehazing for image and video using guided filter. Appl Sci 2(4B):123–127

    Article  Google Scholar 

  16. Liu Z, Xie F (2018)Co-saliency Detection for RGBD Images Based on Multi-constraint Superpixels Matching and Co-cellular Automata. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, Cham, pp 132–143

  17. Long J, Shi Z, Tang W (2012) Fast haze removal for a single remote sensing image using dark channel prior. In: International Conference on Computer Vision in Remote Sensing. IEEE, pp 132–135

  18. Lu H, Li Y, Nakashima S, Serikawa S (2016) Single dahazing through improved atmospheric light estimation. Multimed Tools Appl 75(24):17081–17096

    Article  Google Scholar 

  19. Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient dahazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE international conference on computer vision, pp 617–624

  20. Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: A survey. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2021.3059968

  21. Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724

    Article  Google Scholar 

  22. Negru M, Nedevschi S, Peter RI (2015) Exponential contrast restoration in fog conditions for driving assistance. IEEE Trans Intell Transp Syst 16(4):2257–2268

    Article  Google Scholar 

  23. Raj RJS, Shobana SJ, Pustokhina IV, Pustokhin DA, Gupta D, Shankar K (2020) Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8:58006–58017

    Article  Google Scholar 

  24. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang MH (2016) Single dahazing via multi-scale convolutional neural networks. In: European conference on computer vision. Springer, Cham, pp 154–169

  25. Sandeep MS (2014) Remote sensing dahazing using guided filter. Int J Res Stud Comput Sci Eng 1:44–49

    Google Scholar 

  26. Singh D, Kumar V (2018) Comprehensive survey on haze removal techniques. Multimed Tools Appl 77(8):9595–9620

    Article  Google Scholar 

  27. Singh D, Garg D, Singh Pannu H (2017) Efficient landsat image fusion using fuzzy and stationary discrete wavelet transform. Imaging Sci J 65(2):108–114

    Article  Google Scholar 

  28. Tan RT (2008) Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 1–8

  29. Tan DS, Chen WY, Hua KL (2018) DeepDemosaicking: Adaptive image demosaicking via multiple deep fully convolutional networks. IEEE Trans Image Process 27(5):2408–2419

    Article  MathSciNet  MATH  Google Scholar 

  30. Yin W, Mei T, Chen CW, Li S (2013) Socialized mobile photography: Learning to photograph with social context via mobile devices. IEEE Trans Multimed 16(1):184–200

    Article  Google Scholar 

  31. Zhang S, He F, Ren W, Yao J (2020) Joint learning of image detail and transmission map for single dahazing. Vis Comput 36(2):305–316

    Article  Google Scholar 

  32. Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533

Download references

Author information

Author notes

  1. Harish Babu G

    Present address: Department of ECE, CVR College of Engineering, Hyderabad, India

  2. Venkatram N

    Present address: Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India

Authors and Affiliations

  1. Department of ECE, Koneru Laskshmaiah Education Foundation, Guntur, India

    Harish Babu G

Authors
  1. Harish Babu G
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Venkatram N
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harish Babu G.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval and human participation

No ethics approval is required.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

G, H.B., N, V. An efficient image dahazing using Googlenet based convolution neural networks. Multimed Tools Appl 81, 43897–43917 (2022). https://doi.org/10.1007/s11042-022-13222-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13222-2

Keywords

Search

Navigation