Skip to main content
Log in

Bounding function for fast computation of transmission in single image dehazing

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

Abstract

There exist multiple dehazed images corresponding to a single hazy image due to ill-posed nature of single image dehazing (SID), making it a challenging problem. Usually, the SID used atmospheric scattering model (ASM) to obtain haze-free image from a hazy image. According to ASM, recovery of lost visibility depends upon accurate transmission. The proposed method presents a linear multiplicative bounding function (MBF) for estimation of difference channel (DC) to compute the value of transmission. The results obtained by the MBF has been compared with renowned SID methods. The accuracy of the proposed MBF has been proved by visual and objective evaluation of the dehazed images.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  1. Berman D, Treibitz T, Avidan S (2016) Non-local image dehazing. In: IEEE Conference on computer vision and pattern recognition, pp 1674–1682

  2. 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  Google Scholar 

  3. Engin D, Genc A, Ekenel H (2018) Cycle-dehaze: Enhanced cyclegan for single image dehazing. In: IEEE Conference on computer vision and pattern recognition, pp 938–9388

  4. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  5. He K, Sun J, Tang X (2012) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  6. Jha DK, Gupta B, Lamba SS (2016) L2-norm-based prior for haze-removal from single image. IET Comput Vis 10(5):331–341

    Article  Google Scholar 

  7. Kim J-H, Jang W-D, Sim J-Y, Kim C-S (2013) Optimized contrast enhancement for real-time image and video dehazing. J Vis Commun Image Represent 24(3):410–425

    Article  Google Scholar 

  8. Li C, Guo C, Guo J, Han P, Fu H, Cong R (2019) Pdr-net: Perception-inspired single image dehazing network with refinement. IEEE Trans Multimed:1–1

  9. Li C, Guo J, Porikli F, Fu H, Pang Y (2018) A cascaded convolutional neural network for single image dehazing. IEEE Access 6:24 877–24 887

    Article  Google Scholar 

  10. Li Y, Miao Q, Song J, Quan Y, Li W (2016) Single image haze removal based on haze physical characteristics and adaptive sky region detection. Neurocomputing 182:221–234. [Online] Available: http://www.sciencedirect.com/science/article/pii/S0925231215019694

  11. Li B, Peng X, Wang Z, Xu J, Feng D (2017) Aod-net: All-in-one dehazing network. In: IEEE International conference on computer vision, pp 4780–4788

  12. Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2019) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492–505

    Article  MathSciNet  Google Scholar 

  13. Ling Z, Fan G, Gong J, Wang Y, Lu X (2017) Perception oriented transmission estimation for high quality image dehazing. Neurocomputing 224:82–95. [Online] Available: http://www.sciencedirect.com/science/article/pii/S0925231216312917

  14. Liu S, Rahman MA, Liu SC, Wong CY, C-F Lin H. W. u., Kwok N (2016) Image de-hazing from the perspective of noise filtering. Comput. Electr. Eng. 62:345–359. [Online] Available: http://www.sciencedirect.com/science/article/pii/S0045790616308266

  15. Lu H, Li Y, Xu X, He L, Li Y, Dansereau D, Serikawa S (2016) Underwater image descattering and quality assessment. In: IEEE International conference on image processing, pp 1998–2002

  16. Ma K, Liu W, Wang Z (2015) Perceptual evaluation of single image dehazing algorithms. In: IEEE International conference on image processing

  17. Mantiuk R (2011) Hdr-vdp-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. In: ACM SIGGRAPH 2011 Papers, ser. SIGGRAPH ’11. ACM, New York, pp 40:1–40:14. [Online]. Available: https://doi.org/10.1145/1964921.1964935

  18. Mantiuk R, Kim KJ, Rempel AG, Heidrich W (2011) Hdr-vdp-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans Graph 30(4):40:1–40:14. [Online]. Available: https://doi.org/10.1145/2010324.1964935

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

  20. Narasimhan SG (2004) Models and algorithms for vision through the atmosphere. Ph.D. dissertation, New York

  21. Nathan Silberman PK, Hoiem D, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: European conference on computer vision, vol 7576, pp 746–760

  22. Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: IEEE Conference on computer vision, pp 820–827

  23. Raikwar SC (2019) Adaptive dehazing control factor based fast single image dehazing. Multimedia Tools and Applications, pp 891–918. [Online] Available: https://doi.org/10.1007/s11042-019-08120-z

  24. Raikwar SC, Tapaswi S (2017) An improved linear depth model for single image fog removal. Multimed Tools Appl 77(15):19 719–19 744

    Article  Google Scholar 

  25. Raikwar SC, Tapaswi S (2018) Tight lower bound on transmission for single image dehazing. The Visual Computer. [Online] Available: https://doi.org/10.1007/s00371-018-1596-5

  26. Raikwar SC, Tapaswi S (2020) Lower bound on transmission using non-linear bounding function in single image dehazing. IEEE Trans Image Process 29:4832–4847

    Article  Google Scholar 

  27. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M. -H. (2016) Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision, pp 154–169

  28. Santra S, Mondal R, Chanda B (2018) Learning a patch quality comparator for single image dehazing. IEEE Trans Image Process 27(9):4598–4607

    Article  MathSciNet  Google Scholar 

  29. Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0045790613002644

  30. Shi LF, Chen BH, Huang SC, Larin A, Seredin O, Kopylov A, Kuo SY (2018) Removing haze particles from single image via exponential inference with support vector data description. IEEE Trans Multimed 20(9):2503–2512

    Article  Google Scholar 

  31. Song Y, Li J, Wang X, Chen X (2018) Single image dehazing using ranking convolutional neural network. IEEE Trans Multimed 20(6):1548–1560

    Article  Google Scholar 

  32. Tan R (2018) Visibility in bad weather from a single image. In: IEEE Conference on computer vision and pattern recognition, pp 24–26

  33. Tang K, Yang J, Wang J (2014) Investigating haze-relevant features in a learning framework for image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2995–3002

  34. Tarel JP, Hautière N (2009) Fast visibility restoration from a single color or gray level image. In: IEEE International conference on computer vision, pp 2201–2208

  35. Wang R, Li R, sun H (2016) Haze removal based on multiple scattering model with superpixel algorithm. J Signal Process 127(C):24–36

    Article  Google Scholar 

  36. Wang J, Wang W, Wang R, Gao W (2016) Csps: an adaptive pooling method for image classification. IEEE Trans Multimed 18(6):1000–1010

    Article  Google Scholar 

  37. Wang W, Yuan X, Wu X, Liu Y (2017) Dehazing for images with large sky region. Neurocomputing 238(Supplement C):365–376. [Online] Available: http://www.sciencedirect.com/science/article/pii/S0925231217302412

  38. Wang W, Yuan X, Wu X, Liu Y (2017) Fast image dehazing method based on linear transformation. IEEE Trans Multimed 19(6):1142–1155

    Article  Google Scholar 

  39. Xiao C, Gan J (2012) Fast image dehazing using guided joint bilateral filter. Vis Comput Int J Comput Graph 28(6-8):713–721

    Google Scholar 

  40. Xu Y, Wen J, Fei L, Zhang Z (2015) Review of video and image defogging algorithms and related studies on image restoration and enhancement. IEEE Access 4:165–188

    Article  Google Scholar 

  41. Yang M, Liu J, Li Z (2018) Super-pixel based single nighttime image haze removal. IEEE Trans Multimed 20(11):3008–3018

    Article  Google Scholar 

  42. Yang D, Sun J (2018) Proximal dehaze-net: a prior learning-based deep network for single image dehazing. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) European conference on computer vision. Springer International Publishing, Cham, pp 729–746

  43. Yuan F, Huang H (2018) Image haze removal via reference retrieval and scene prior. IEEE Trans Image Process 27(9):4395–4409

    Article  MathSciNet  Google Scholar 

  44. Yuan H, Liu C, Guo Z, Sun Z (2017) A region-wised medium transmission based image dehazing method. IEEE Access 5:1735–1742

    Article  Google Scholar 

  45. Zhang Y. -Q., Ding Y, Xiao J. -S., Liu J, Guo Z (2012) Visibility enhancement using an image filtering approach. EURASIP J Adv Signal Process 2012(1):220–225

    Article  Google Scholar 

  46. Zhang H, Patel VM (2018) Densely connected pyramid dehazing network. In: IEEE Conference on computer vision and pattern recognition, pp 3194–3203

  47. 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

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suresh Chandra Raikwar.

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

Raikwar, S.C., Tapaswi, S. & Chakraborty, S. Bounding function for fast computation of transmission in single image dehazing. Multimed Tools Appl 81, 5349–5372 (2022). https://doi.org/10.1007/s11042-021-11752-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11752-9

Keywords

Navigation