research-article

Detail-Enhanced Multi-Scale Exposure Fusion in YUV Color Space

Authors:

Zhengguo LiAuthors Info & Claims

IEEE Transactions on Circuits and Systems for Video Technology, Volume 30, Issue 8

Pages 2418 - 2429

https://doi.org/10.1109/TCSVT.2019.2919310

Published: 01 August 2020 Publication History

Abstract

It is recognized that existing multi-scale exposure fusion algorithms can be improved using edge-preserving smoothing techniques. However, the complexity of edge-preserving smoothing-based multi-scale exposure fusion is an issue for mobile devices. In this paper, a simpler multi-scale exposure fusion algorithm is designed in YUV color space. The proposed algorithm can preserve details in the brightest and darkest regions of a high dynamic range (HDR) scene and the edge-preserving smoothing-based multi-scale exposure fusion algorithm while avoiding color distortion from appearing in the fused image. The complexity of the proposed algorithm is about half of the edge-preserving smoothing-based multi-scale exposure fusion algorithm. The proposed algorithm is thus friendlier to the smartphones than the edge-preserving smoothing-based multi-scale exposure fusion algorithm. In addition, a simple detail-enhancement component is proposed to enhance fine details of fused images. The experimental results show that the proposed component can be adopted to produce an enhanced image with visibly enhanced fine details and a higher MEF-SSIM value. This is impossible for existing detail enhancement components. Clearly, the component is attractive for PC-based applications.

References

[1]

P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in Proc. 24th Annu. Conf. Comput. Graph. Interact. Techn., Aug. 1997, pp. 369–378.

[2]

F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” in Proc. 29th Annu. Conf. Comput. Graph. Interact. Techn., San Antonio, TX, USA, Jul. 2002, pp. 257–266.

[3]

Z. Farbman, R. Fattal, D. Lischinshi, and R. Szeliski, “Edge-preserving decompostions for multi-scale tone and details manipulation,” ACM Trans. Graph., vol. 27, pp. 249–256, Aug. 2008.

[4]

Z. Li and J. Zheng, “Visual-salience-based tone mapping for high dynamic range images,” IEEE Trans. Ind. Electron., vol. 61, no. 12, pp. 7076–7082, Dec. 2014.

[5]

G. Eilertsen, R. Wanat, R. K. Mantiuk, and J. Unger, “Evaluation of tone mapping operators for HDR video,” Comput. Graph. Forum, vol. 32, no. 7, pp. 275–284, 2013.

[6]

A. Abd-el-kader, H. E.-D. Moustafa, and S. Rehan, “Performance measures for image fusion based on wavelet transform and curvelet transform,” in Proc. 28th Nat. Radio Sci. Conf. (NRSC), Apr. 2011, pp. 1–7.

[7]

J. Xu, Y. Huang, and J. Wang, “Multi-exposure images of wavelet transform fusion,” Proc. SPIE, vol. 8878, 2013, Art. no.

[8]

M. Li and Y. Dong, “Review of image fusion algorithm based on multiscale decomposition,” in Proc. Int. Conf. Mech. Sci., Electr. Eng. Comput. (MEC), Dec. 2013, pp. 1422–1425.

[9]

I. Merianos and N. Mitianoudis, “A hybrid multiple exposure image fusion approach for HDR image synthesis,” in Proc. IEEE Int. Conf. Imag. Syst. Techn. (IST), Oct. 2016, pp. 222–226.

[10]

M. Nejati, M. Karimi, S. M. R. Soroushmehr, N. Karimi, S. Samavi, and K. Najarian, “Fast exposure fusion using exposedness function,” in Proc. IEEE Int. Conf. Image Process., Sep. 2017, pp. 2234–2238.

[11]

T. Kartalov and Z. Ivanovski, “High quality exposure fusion for mobile platforms,” in Proc. IEEE Int. Conf. Smart Technol. (EUROCON), Jul. 2017, pp. 297–300.

[12]

T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion,” in Proc. 15th Pacific Conf. Comput. Graph. Appl., Oct. 2007, pp. 382–390.

[13]

A. A. Goshtasby and S. Nikolov, “Guest editorial: Image fusion: Advances in the state of the art,” Inf. Fusion, vol. 8, no. 2, pp. 114–118, Apr. 2007.

Digital Library

[14]

P. Burt and E. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun., vol. COM-31, no. 4, pp. 532–540, Apr. 1983.

[15]

K. Ma, K. Zeng, and Z. Wang, “Perceptual quality assessment for multi-exposure image fusion,” IEEE Trans. Image Process., vol. 24, no. 11, pp. 3345–3356, Nov. 2015.

Digital Library

[16]

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. 6th IEEE Int. Conf. Comput. Vis., Jan. 1998, pp. 839–846.

[17]

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1397–1409, Jun. 2013.

Digital Library

[18]

Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted guided image filtering,” IEEE Trans. Image Process., vol. 24, no. 1, pp. 120–129, Jan. 2015.

[19]

F. Kou, W. Chen, C. Wen, and Z. Li, “Gradient domain guided image filtering,” IEEE Trans. Image Process., vol. 24, no. 11, pp. 4528–4539, Nov. 2015.

Digital Library

[20]

S. Li, X. Kang, and J. Hu, “Image fusion with guided filtering,” IEEE Trans. Image Process., vol. 22, no. 7, pp. 2864–2875, Jul. 2013.

[21]

Z. Li, Z. Wei, C. Wen, and J. Zheng, “Detail-enhanced multi-scale exposure fusion,” IEEE Trans. Image Process., vol. 26, no. 3, pp. 1243–1252, Mar. 2017.

Digital Library

[22]

F. Kou, Z. Li, C. Wen, and W. Chen, “Multi-scale exposure fusion via gradient domain guided image filtering,” in Proc. IEEE Int. Conf. Multimedia Expo (ICME), Jul. 2017, pp. 1105–1110.

[23]

J. Cai, S. Gu, and L. Zhang, “Learning a deep single image contrast enhancer from multi-exposure images,” IEEE Trans. Image Process., vol. 27, no. 4, pp. 2049–2062, Apr. 2018.

[24]

Y. Liu, D. Wang, J. Zhang, and X. Chen, “A fast fusion method for multi-exposure image in YUV color space,” in Proc. IEEE 3rd Adv. Inf. Technol., Electron. Autom. Control Conf. (IAEAC), Oct. 2018, pp. 1685–1689.

[25]

C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, and A. C. Bovik, “Single-scale fusion: An effective approach to merging images,” IEEE Trans. Image Process., vol. 26, no. 1, pp. 65–78, Jan. 2017.

Digital Library

[26]

R. Shen, I. Cheng, J. Shi, and A. Basu, “Generalized random walks for fusion of multi-exposure images,” IEEE Trans. Image Process., vol. 20, no. 12, pp. 3634–3646, Dec. 2011.

Digital Library

[27]

M. Song, D. Tao, C. Chen, J. Bu, J. Luo, and C. Zhang, “Probabilistic exposure fusion,” IEEE Trans. Image Process., vol. 21, no. 1, pp. 341–357, Jan. 2012.

Digital Library

[28]

Z. G. Li, J. H. Zheng, and S. Rahardja, “Detail-enhanced exposure fusion,” IEEE Trans. Image Process., vol. 21, no. 11, pp. 4672–4676, Nov. 2012.

Digital Library

[29]

S. Di Zenzo, “A note on the gradient of a multi-image,” Comput. Vis., Graph. Image Process., vol. 33, no. 1, pp. 116–125, Jan. 1986.

Digital Library

[30]

F. Kou, W. Chen, X. Wu, and Z. Li, “Intelligent detail enhancement for differently exposed images,” in Proc. IEEE Int. Conf. Image Process. (ICIP), Sep. 2017, pp. 3185–3189.

[31]

F. Kou, Z. Li, C. Wen, and W. Chen, “Edge-preserving smoothing pyramid based multi-scale exposure fusion,” J. Vis. Commun. Image Represent., vol. 53, pp. 235–244, May 2018.

[32]

Q. Wang, W. Chen, X. Wu, and Z. Li, “Detail preserving multi-scale exposure fusion,” in Proc. 25th IEEE Int. Conf. Image Process. (ICIP), Oct. 2018, pp. 1713–1717.

[33]

F. Kou, Z. Wei, W. Chen, X. Wu, C. Wen, and Z. Li, “Intelligent detail enhancement for exposure fusion,” IEEE Trans. Multimedia, vol. 20, no. 2, pp. 484–495, Feb. 2018.

Digital Library

[34]

C. F. Hall and E. L. Hall, “A nonlinear model for the spatial characteristics of the human visual system,” IEEE Trans. Syst., Man, Cybern., Syst., vol. SMC-7, no. 3, pp. 161–170, Mar. 1977.

[35]

D. Min, S. Choi, J. Lu, B. Ham, K. Sohn, and M. Do, “Fast global image smoothing based on weighted least squares,” IEEE Trans. Image Process., vol. 23, no. 12, pp. 5638–5653, Dec. 2014.

Cited By

Hong WZhang HMa J(2024)OFPF-MEF: An Optical Flow Guided Dynamic Multi-Exposure Image Fusion Network With Progressive Frequencies LearningIEEE Transactions on Multimedia10.1109/TMM.2024.337988326(8581-8595)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3379883
Liu ZLiu JWu GChen ZFan XLiu R(2024)Searching a Compact Architecture for Robust Multi-Exposure Image FusionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.335193334:7(6224-6237)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3351933
Peng PJing ZPan HLiu YSong B(2024)CurveMEFNeurocomputing10.1016/j.neucom.2024.127915596:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127915
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Index Terms

Detail-Enhanced Multi-Scale Exposure Fusion in YUV Color Space
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Circuits and Systems for Video Technology

IEEE Transactions on Circuits and Systems for Video Technology Volume 30, Issue 8

Aug. 2020

499 pages

ISSN:1051-8215

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1051-8215 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 August 2020

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Cited By

Hong WZhang HMa J(2024)OFPF-MEF: An Optical Flow Guided Dynamic Multi-Exposure Image Fusion Network With Progressive Frequencies LearningIEEE Transactions on Multimedia10.1109/TMM.2024.337988326(8581-8595)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3379883
Liu ZLiu JWu GChen ZFan XLiu R(2024)Searching a Compact Architecture for Robust Multi-Exposure Image FusionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.335193334:7(6224-6237)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3351933
Peng PJing ZPan HLiu YSong B(2024)CurveMEFNeurocomputing10.1016/j.neucom.2024.127915596:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127915
Chang RLiu GTang HQian YTang J(2024)RDGMEF: a multi-exposure image fusion framework based on Retinex decompostion and guided filterNeural Computing and Applications10.1007/s00521-024-09779-836:20(12083-12102)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09779-8
Deng XXu JGao FSun XXu M(2023)Deep$\mathrm {M^{2}}$M2CDL: Deep Multi-Scale Multi-Modal Convolutional Dictionary Learning NetworkIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.333462446:5(2770-2787)Online publication date: 20-Nov-2023
https://dl.acm.org/doi/10.1109/TPAMI.2023.3334624
Tan XChen HZhang RWang QKan YZheng JJin YChen E(2023)Deep Multi-Exposure Image Fusion for Dynamic ScenesIEEE Transactions on Image Processing10.1109/TIP.2023.331512332(5310-5325)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3315123
Luo JRen WGao XCao X(2023)Multi-Exposure Image Fusion via Deformable Self-AttentionIEEE Transactions on Image Processing10.1109/TIP.2023.324282432(1529-1540)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3242824
Liu ZLi ZWu XLiu ZChen W(2022)DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial NetworksIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.318843332:11(7418-7431)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1109/TCSVT.2022.3188433
Liu JShang JLiu RFan X(2022)Attention-Guided Global-Local Adversarial Learning for Detail-Preserving Multi-Exposure Image FusionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.314445532:8(5026-5040)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1109/TCSVT.2022.3144455
Shen WCheng MLu GZhai GChen LAsif MGao Z(2022)Spatial Temporal Video Enhancement Using Alternating ExposuresIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2021.313533732:8(4912-4926)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1109/TCSVT.2021.3135337

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