Skip to main content
SpringerLink
Log in

Detail enhancement decolorization algorithm based on rolling guided filtering

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

Abstract

An important goal of color image gray-scale is to keep the edge details of the original color image as much as possible. In many cases, the degree of feature discrimination is maintained, but in some cases, edge details are still lost or blurred. Therefore, this paper first uses an improved non-linear global mapping grayscale method to grayscale the color image, and then proposes a grayscale image detail enhancement algorithm based on rolling guided filtering. The method in this paper is to enhance the edge details of the grayscale image by rolling guided filter processing on the basis of the grayscale image. In addition, the rolling-guided filter is a local linear model with better edge retention characteristics, which can overcome the defect that other filters are prone to gradient flips on the edges where the gray level of the image changes sharply, causing the image to appear “false edges”. The experimental results show that when the traditional method loses or blurs the detailed features, the method in this paper can maintain better detailed features.

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

Similar content being viewed by others

References

  1. Ancuti CO, Ancuti C, Bekaert P (2011) Enhancing by saliency-guided decolorization. In: IEEE conference on computer vision and pattern recognition, pp 257–264

  2. Ancuti C, Ancuti CO, De Vleeschouwer C, Sbert M (2018) Decolorization by fusion. IEEE Access 6:64071–64084

    Article  Google Scholar 

  3. Ancuti C, Ancuti CO, Feixas M, Sbert M (2019) Image decolorization based on information theory. In: 2019 IEEE international conference on image processing (ICIP), pp 3242–3246

  4. Ancuti C, Ancuti CO (2016) Laplacian-guided image decolorization. In: IEEE international conference on image processing, pp 4107–4111

  5. Arpit D, Namboodir A (2011) Fingerprint feature extraction from gray scale images by ridge tracing. In: 2011 international joint conference on biometrics (IJCB), pp 1–8

  6. Cai B, Xu X, Xing X (2018) Perception preserving decolorization. In: 2018 25th IEEE international conference on image processing (ICIP), pp 2810–2814

  7. Chan SH, Zickler T, Lu YM (2017) Understanding symmetric smoothing filters: a gaussian mixture model perspective. IEEE Transactions on Image Processing 26:5107–5121

    Article  MathSciNet  Google Scholar 

  8. Chen S, Shi D, Sadiq M, Cheng X (2020) Image denoising With generative adversarial networks and its application to cell image enhancement. IEEE Access 8:82819–82831

    Article  Google Scholar 

  9. Du H, He S, Sheng B, Ma L, Lau RWH (2015) Saliency-guided color-to-gray conversion using region-based optimization. IEEE Trans Image Process 24:434–443

    Article  MathSciNet  Google Scholar 

  10. Guan X, He L, Li M, Li F (2019) Entropy based data expansion method for blind image quality assessment. Entropy 22:60

    Article  Google Scholar 

  11. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 35:1397–1409

    Article  Google Scholar 

  12. Hu J, Zhou X, Ren C, Li X, He X (2020) Image deblocking via shape-adaptive low-rank prior and sparsity-based detail enhancement. Signal Processing Image Communication 86:115874

    Article  Google Scholar 

  13. Ji Z, Fang M, Wang Y, Ma W (2016) Efficient decolorization preserving dominant distinctions. Visual Comput 32:1621–1631

    Article  Google Scholar 

  14. Jian L, Yang X, Zhou Z, Zhou K, Liu K (2018) Multi-scale image fusion through rolling guidance filter. Future Generation Computer Systems 83:310–325

    Article  Google Scholar 

  15. Kang X, Duan P, Li S, Benediktsson JA (2018) Decolorization-based hyperspectral image visualization. IEEE Transactions on Geoscience and Remote Sensing 56:4346–4360

    Article  Google Scholar 

  16. Khan TM, Bailey DG, Khan MAU, Kong Y (2017) Efficient hardware implementation for fingerprint image enhancement using anisotropic gaussian filter. IEEE Transactions on Image Processing 26:2116–2126

    Article  MathSciNet  Google Scholar 

  17. Kim Y, Demouth J, Jang C, Lee S (2009) Robust color-to-gray via nonlinear global mapping. ACM Transactions on Graphics 28:161

    Google Scholar 

  18. Li Z, Zheng J, Zhu Z, Yao W, Wu S (2015) Weighted guided image filtering. IEEE Transactions on Image Processing 24:120–129

    Article  MathSciNet  Google Scholar 

  19. Li C, Liu J, Liu A, Wu Q, Bi L (2019) Global and adaptive contrast enhancement for low illumination gray images. IEEE Access 7:163395–163411

    Article  Google Scholar 

  20. Li Y, Guo L, Jin L (2019) A content-aware image retargeting quality assessment method using foreground and global measurement. IEEE Access 7:91912–91923

    Article  Google Scholar 

  21. Liang Z, Wang Y, Ding X, Mi Z, Fu X (2020) Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing. Neurocomputing, pp 1–19

  22. Lien C, Tang C, Chen P, Kuo Y (2020) A low-cost VLSI architecture of the bilateral filter for real-time image denoising. IEEE Access 8:64278–64283

    Article  Google Scholar 

  23. Liu Q, Leung H (2019) Variable augmented neural network for decolorization and multi-exposure fusion. Information Fusion 46:114–127

    Article  Google Scholar 

  24. Liu S, Zhang X (2019) Image decolorization combining local features and exposure features. IEEE Trans Multimed 21:2461–2472

    Article  Google Scholar 

  25. Liu Q, Liu PX, Xie W, Wang Y, Liang D (2015) GcsDecolor: gradient correlation similarity for efficient contrast preserving decolorization. IEEE Transactions on Image Processing 24:2889–2904

    Article  MathSciNet  Google Scholar 

  26. Liu Q, Shao G, Wang Y, Gao J, Leung H (2017) Log-euclidean metrics for contrast preserving decolorization. IEEE Transactions on Image Processing 26:5772–5783

    Article  MathSciNet  Google Scholar 

  27. Liu Q, Liu P, Wang Y, Leung H (2017) Semi-parametric decolorization with laplacian-based perceptual quality metric. IEEE Transactions on Circuits and Systems for Video Technology 27:1856–1868

    Google Scholar 

  28. Liu Q, Li S, Xiong J, Qin B (2019) WpmDecolor: weighted projection maximum solver for contrast-preserving decolorization. The Visual Computer 35:205–221

    Article  Google Scholar 

  29. Makinen Y, Azzari L, Foi A (2019) Exact transform-domain noise variance for collaborative filtering of stationary correlated noise. In: 2019 IEEE international conference on image processing (ICIP), pp 185–189

  30. Porikli F (2019) Constant time o (1) bilateral filtering. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1–8

  31. Rumambi T, Hustinawaty H, Madenda S, Wibowo EP (2017) Measurement straight leg raise for low back pain based grayscale depth. TELKOMNIKA (Telecommunication Computing Electronics and Control) 15:471–477

    Article  Google Scholar 

  32. Sirichotedumrong W, Chuman T, Imaizumi S, Kiya H (2018) Grayscale-based block scrambling image encryption for social networking services. In: 2018 IEEE international conference on multimedia and expo (ICME), pp 1–6

  33. Subramani B, Veluchamy M (2020) Quadrant dynamic clipped histogram equalization with gamma correction for color image enhancement. Color Research and Application 45:644–655

    Article  Google Scholar 

  34. Tao Y, Shen Y, Sheng B, Li P, Lau RWH (2018) Video decolorization using visual proximity coherence optimization. IEEE Transactions on Cybernetics 48:1406–1419

    Article  Google Scholar 

  35. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: International conference on computer vision (ICCV), pp 839–846

  36. Wang W, Li Z, Wu S (2018) Color contrast-preserving decolorization. IEEE Transactions on Image Processing 27:5464–5474

    Article  MathSciNet  Google Scholar 

  37. Wang J, Shi K, Wang L, Li Z, Pan R, Gao W (2019) Decoloration of multi-color fabric images for fabric appearance smoothness evaluation by supervised image-to-image translation. IEEE Access 7:181284–181294

    Article  Google Scholar 

  38. Wang W, Li Z, Wu S, Zeng L (2020) Hazy image decolorization with color contrast restoration. IEEE Transactions on Image Processing 29:1776–1787

    Article  MathSciNet  Google Scholar 

  39. Xie Y, Richmond D (2018) Pre-training on grayscale ImageNet improves medical image classification. In: ECCV 2018: computer vision – ECCV 2018 workshops, pp 476–484

  40. Yangping W, Shaowei X, Zhengping Z, Yue S (2018) Real-time defect setection method for printed images based on grayscale and gradient differences. J Eng Sci Technol Rev 11:180–188

    Article  Google Scholar 

  41. Zhang Y, Huang W, Bi W, Gao G (2016) Colorful image enhancement algorithm based on guided filter and Retinex. In: 2016 IEEE international conference on signal and image processing, pp 33–36

  42. Zhang Q, Shen X, Xu L, Jia J (2014) Rolling guidance filter. In: European conference on computer vision, pp 815–830

Download references

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant nos. 61772319, 62002200, 61976125, 61976124), and Shandong Natural Science Foundation of China (Grant no. ZR2017MF049).

Author information

Authors and Affiliations

  1. School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264005, China

    Nana Yu & Zhen Hua

  2. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, China

    Jinjiang Li

Authors
  1. Nana Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinjiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, N., Li, J. & Hua, Z. Detail enhancement decolorization algorithm based on rolling guided filtering. Multimed Tools Appl 81, 2711–2731 (2022). https://doi.org/10.1007/s11042-021-11677-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11677-3

Keywords

Search

Navigation