[1] Cui Z L, Sheng H, Yang D et al. Light field depth estimation for non-lambertian objects via adaptive cross operator[J]. IEEE Trans Circuits Syst Video Technol, 34, 1199-1211(2024).

[2] Ma S, Wang N, Zhu L C et al. Light field depth estimation using weighted side window angular coherence[J]. Opto-Electron Eng, 48, 210405(2021).

[3] Wu D, Zhang X D, Fan Z G et al. Depth acquisition of noisy scene based on inline occlusion handling of light field[J]. Opto-Electron Eng, 48, 200422(2021).

[4] Cong R X, Yang D, Chen R S et al. Combining implicit-explicit view correlation for light field semantic segmentation[C], 9172-9181(2023). https://doi.org/10.1109/CVPR52729.2023.00885

[5] Han L, Zhong D W, Li L et al. Learning residual color for novel view synthesis[J]. IEEE Trans Image Process, 31, 2257-2267(2022).

[6] Xu F, Liu J H, Song Y M et al. Multi-exposure image fusion techniques: a comprehensive review[J]. Remote Sens, 14, 771(2022).

[7] Li S T, Kang X D, Hu J W. Image fusion with guided filtering[J]. IEEE Trans Image Process, 22, 2864-2875(2013).

[8] Liu Y, Wang Z F. Dense SIFT for ghost-free multi-exposure fusion[J]. J Visual Commun Image Represent, 31, 208-224(2015).

[9] Lee S, 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). https://doi.org/10.1109/ICIP.2018.8451153

[10] Ulucan O, Ulucan D, Turkan M. Ghosting-free multi-exposure image fusion for static and dynamic scenes[J]. Signal Process, 202, 108774(2023).

[11] Gul M S K, Wolf T, Bätz M et al. A high-resolution high dynamic range light-field dataset with an application to view synthesis and tone-mapping[C], 1-6(2020). https://doi.org/10.1109/ICMEW46912.2020.9105964

[12] Li C, Zhang X. High dynamic range and all-focus image from light field[C], 7-12(2015). https://doi.org/10.1109/ICCIS.2015.7274539

[13] Le Pendu M, Guillemot C, Smolic A. High dynamic range light fields via weighted low rank approximation[C], 1728-1732(2018). https://doi.org/10.1109/ICIP.2018.8451584

[14] Yin J L, Chen B H, Peng Y T. Two exposure fusion using prior-aware generative adversarial network[J]. IEEE Trans Multimedia, 24, 2841-2851(2021).

[15] Xu H, Ma J Y, Zhang X P. MEF-GAN: multi-exposure image fusion via generative adversarial networks[J]. IEEE Trans Image Process, 29, 7203-7216(2020).

[16] Liu J Y, Wu G Y, Luan J S et al. HoLoCo: holistic and local contrastive learning network for multi-exposure image fusion[J]. Inf Fusion, 95, 237-249(2023).

[17] Liu J Y, Shang J J, Liu R S et al. Attention-guided global-local adversarial learning for detail-preserving multi-exposure image fusion[J]. IEEE Trans Circuits Syst Video Technol, 32, 5026-5040(2022).

[18] Chen Y Y, Jiang G Y, Yu M et al. Learning to simultaneously enhance field of view and dynamic range for light field imaging[J]. Inf Fusion, 91, 215-229(2023).

[19] Ram Prabhakar K, Sai Srikar V, Venkatesh Babu R. DeepFuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs[C], 4724-4732(2017). https://doi.org/10.1109/ICCV.2017.505

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

[21] Qu L H, Liu S L, Wang M N et al. TransMEF: a transformer-based multi-exposure image fusion framework using self-supervised multi-task learning[C], 2126-2134(2022). https://doi.org/10.1609/AAAI.v36i2.20109

[22] Zheng K W, Huang J, Yu H et al. Efficient multi-exposure image fusion via filter-dominated fusion and gradient-driven unsupervised learning[C], 2804-2813(2023). https://doi.org/10.1109/CVPRW59228.2023.00281

[23] Xu H, Haochen L, Ma JY. Unsupervised multi-exposure image fusion breaking exposure limits via contrastive learning[C], 3010-3017(2023). https://doi.org/10.1609/AAAI.v37i3.25404

[24] Zhang H, Ma J Y. IID-MEF: a multi-exposure fusion network based on intrinsic image decomposition[J]. Inf Fusion, 95, 326-340(2023).

[25] Xu H, Ma J Y, Le Z L et al. FusionDN: a unified densely connected network for image fusion[C], 12484-12491(2020). https://doi.org/10.1609/AAAI.v34i07.6936

[26] Xu H, Ma J Y, Jiang J J et al. U2Fusion: a unified unsupervised image fusion network[J]. IEEE Trans Pattern Anal Mach Intell, 44, 502-518(2022).

[27] Zhang H, Xu H, Xiao Y et al. Rethinking the image fusion: a fast unified image fusion network based on proportional maintenance of gradient and intensity[C], 12797-12804(2020). https://doi.org/10.1609/AAAI.v34i07.6975

[28] Zhou M, Huang J, Fang Y C et al. Pan-sharpening with customized transformer and invertible neural network[C], 3553-3561(2022). https://doi.org/10.1609/aaai.v36i3.20267

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

[30] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 13, 600-612(2004).

[31] Hossny M, Nahavandi S, Creighton D. Comments on ‘Information measure for performance of image fusion’[J]. Electron Lett, 44, 1066-1067(2008).

[32] Wang Q, Shen Y, Jin J. Performance evaluation of image fusion techniques[M]. Stathaki T. Image Fusion: Algorithms and Applications, 469-492(2008). https://doi.org/10.1016/B978-0-12-372529-5.00017-2

[33] Cui G M, Feng H J, Xu Z H et al. Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition[J]. Opt Commun, 341, 199-209(2015).

[34] Xydeas C S, Petrovic V. Objective image fusion performance measure[J]. Electronics letters, 36, 308-309(2000).

[35] Rao Y J. In-fibre Bragg grating sensors[J]. Meas Sci Technol, 8, 355-375(1997).

[36] Eskicioglu A M, Fisher P S. Image quality measures and their performance[J]. IEEE Trans Commun, 43, 2959-2965(1995). https://doi.org/10.1109/26.477498

[37] Chen H, Varshney P K. A human perception inspired quality metric for image fusion based on regional information[J]. Inf Fusion, 8, 193-207(2007).