• Laser & Optoelectronics Progress
  • Vol. 59, Issue 8, 0811008 (2022)
Weihua Liu and Biyan Ma*
Author Affiliations
  • School of Communication and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an , Shaanxi 710121, China
  • show less
    DOI: 10.3788/LOP202259.0811008 Cite this Article Set citation alerts
    Weihua Liu, Biyan Ma. Multiexposure Image Fusion Method Based on Feature Weight of Image Sequence[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0811008 Copy Citation Text show less
    References

    [1] Banterle F, Artusi A, Debattista K et al[M]. Advanced high dynamic range imaging: theory and practice(2011).

    [2] Banterle F, Ledda P, Debattista K et al. Inverse tone mapping[C], 349-356(2006).

    [3] Masia B, Agustin S, Fleming R W et al. Evaluation of reverse tone mapping through varying exposure conditions[J]. ACM Transactions on Graphics, 28, 1-8(2009).

    [4] Debevec P E, Malik J. Recovering high dynamic range radiance maps from photographs[C], 369-378(1997).

    [5] Feng W, Liu H D, Wu G M et al. Gradient domain adaptive tone mapping algorithm based on color correction model[J]. Laser & Optoelectronics Progress, 57, 081007(2020).

    [6] Pang Z B, Lu B B, Gu Y N et al. Crossing decomposition based tone mapping algorithm for high dynamic range image[J]. Laser & Optoelectronics Progress, 58, 1410020(2021).

    [7] Lee S, An G H, Kang S J. Deep chain HDRI: reconstructing a high dynamic range image from a single low dynamic range image[J]. IEEE Access, 6, 49913-49924(2018).

    [8] Kinoshita Y, Kiya H. iTM-Net: deep inverse tone mapping using novel loss function considering tone mapping operator[J]. IEEE Access, 7, 73555-73563(2019).

    [9] Prabhakar K R, Srikar V S, Babu R V. DeepFuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs[C], 4724-4732(2017).

    [10] Li H, Zhang L. Multi-exposure fusion with CNN features[C], 1723-1727(2018).

    [11] Zhang Y, Liu Y, Sun P et al. IFCNN: a general image fusion framework based on convolutional neural network[J]. Information Fusion, 54, 99-118(2020).

    [12] Ma K D, Duanmu Z F, Zhu H W et al. Deep guided learning for fast multi-exposure image fusion[J]. IEEE Transactions on Image Processing, 29, 2808-2819(2020).

    [13] Goshtasby A A. Fusion of multi-exposure images[J]. Image and Vision Computing, 23, 611-618(2005).

    [14] Ma K D, Wang Z. Multi-exposure image fusion: a patch-wise approach[C], 1717-1721(2015).

    [15] Ma X Y, Fan F Q, Lu T R et al. Multi-exposure image fusion de-ghosting algorithm based on image block decomposition[J]. Acta Optica Sinica, 39, 0910001(2019).

    [16] Mertens T, Kautz J, van Reeth F. Exposure fusion: a simple and practical alternative to high dynamic range photography[J]. Computer Graphics Forum, 28, 161-171(2009).

    [17] Shen J B, Zhao Y, Yan S C et al. Exposure fusion using boosting Laplacian pyramid[J]. IEEE Transactions on Cybernetics, 44, 1579-1590(2014).

    [18] Lee S H, Park J S, Cho N I. A multi-exposure image fusion based on the adaptive weights reflecting the relative pixel intensity and global gradient[C], 1737-1741(2018).

    [19] Asadi A, Ezoji M. Multi-exposure image fusion via a pyramidal integration of the phase congruency of input images with the intensity-based maps[J]. IET Image Processing, 14, 3127-3133(2020).

    [20] Li S T, Kang X D. Fast multi-exposure image fusion with median filter and recursive filter[J]. IEEE Transactions on Consumer Electronics, 58, 626-632(2012).

    [21] Li S T, Kang X D, Hu J W. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 22, 2864-2875(2013).

    [22] Wang Q T, Chen W H, Wu X M et al. Detail-enhanced multi-scale exposure fusion in YUV color space[J]. IEEE Transactions on Circuits and Systems for Video Technology, 30, 2418-2429(2020).

    [23] Qu Z, Huang X, Liu L. An improved algorithm of multi-exposure image fusion by detail enhancement[J]. Multimedia Systems, 27, 33-44(2021).

    [24] Zeng J X, Zhou L L, Fu X. Complex image line feature extraction based on improved Beamlet transform and the Canny operator[J]. Journal of Image and Graphics, 17, 775-782(2012).

    [25] Ma K D, Zeng K, Wang Z. Perceptual quality assessment for multi-exposure image fusion[J]. IEEE Transactions on Image Processing, 24, 3345-3356(2015).

    [26] Gu B, Li W J, Wong J et al. Gradient field multi-exposure images fusion for high dynamic range image visualization[J]. Journal of Visual Communication and Image Representation, 23, 604-610(2012).

    [27] Song M L, Tao D C, Chen C et al. Probabilistic exposure fusion[J]. IEEE Transactions on Image Processing, 21, 341-357(2012).

    Weihua Liu, Biyan Ma. Multiexposure Image Fusion Method Based on Feature Weight of Image Sequence[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0811008
    Download Citation