[1] MA K D, ZENG K, WANG Z. Perceptual Quality Assessment for Multi-exposure Image Fusion. IEEE Transactions on Image Proce-ssing, 2015, 24(11): 3345-3356.
[2] PRABHAKAR K R, SRIKAR V S, BABU R V. Deepfuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2017: 4724-4732.
[3] DENG X, DRAGOTTI P L. Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 43(10): 3333-3348.
[4] DENG X, ZHANG Y T, XU M, et al. Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution. IEEE Transactions on Image Processing, 2021, 30: 3098-3112.
[5] WARD G. Fast, Robust Image Registration for Compositing High Dynamic Range Photographs from Hand-Held Exposures. Journal of Graphics Tools, 2003, 8(2): 17-30.
[6] ZIMMER H, BRUHN A, WEICKERT J. Freehand HDR Imaging of Moving Scenes with Simultaneous Resolution Enhancement. Computer Graphics Forum, 2011, 30(2): 405-414.
[7] SEN P, KALANTARI N K, YAESOUBI M, et al. Robust Patch-Based HDR Reconstruction of Dynamic Scenes. ACM Transactions on Graphics, 2012, 31(6). DOI: 10.1145/2366145.2366222.
[8] HU J, GALLO O, PULLI K, et al. HDR Deghosting: How to Deal with Saturation? // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 1163-1170.
[9] RAMAN S, CHAUDHURI S. Reconstruction of High Contrast Images for Dynamic Scenes. The Visual Computer, 2011, 27: 1099-1114.
[10] YAN Q S, SUN J Q, LI H S, et al. High Dynamic Range Imaging by Sparse Representation. Neurocomputing, 2017, 269: 160-169.
[11] MA K D, LI H, YONG H W, et al. Robust Multi-exposure Image Fusion: A Structural Patch Decomposition Approach. IEEE Transactions on Image Processing, 2017, 26(5): 2519-2532.
[12] LI H, MA K D, YONG H W, et al. Fast Multi-scale Structural Patch Decomposition for Multi-exposure Image Fusion. IEEE Transac-tions on Image Processing, 2020, 29: 5805-5816.
[13] BAKER S, SCHARSTEIN D, LEWIS J P, et al. A Database and Evaluation Methodology for Optical Flow. International Journal of Computer Vision, 2011, 92. DOI: 10.1007/s11263-010-0390-2.
[14] KALANTARI N K, RAMAMOORTHI R. Deep High Dynamic Range Imaging of Dynamic Scenes. ACM Transactions on Graphics, 2017, 36(4). DOI: 10.1145/3072959.3073609.
[15] WU S Z, XU J R, TAI Y W, et al. Deep High Dynamic Range Imaging with Large Foreground Motions // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 120-135.
[16] YAN Q S, GONG D, SHI Q F, et al. Attention-Guided Network for Ghost-Free High Dynamic Range Imaging // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 1751-1760.
[17] DENG Y P, LIU Q, IKENAGA T. Selective Kernel and Motion-Emphasized Loss Based Attention-Guided Network for HDR Imaging of Dynamic Scenes // Proc of the 25th International Confe-rence on Pattern Recognition. Washington, USA: IEEE, 2021: 8976-8983.
[18] YAN Q S, ZHANG L, LIU Y, et al. Deep HDR Imaging via a Non-local Network. IEEE Transactions on Image Processing, 2020, 29: 4308-4322.
[19] PU Z Y, GUO P Y, ASIF M S, et al. Robust High Dynamic Range(HDR) Imaging with Complex Motion and Parallax // Proc of the Asian Conference on Computer Vision. Berlin, Germany: Sprin-ger, 2020: 134-149
[20] LIU Z, LIN W J, LI X P, et al. ADNet: Attention-Guided Deformable Convolutional Network for High Dynamic Range Imaging // Proc of the IEEE/CVF Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2021: 463-470.
[21] TAN X, CHEN H, XU K, et al. High Dynamic Range Imaging for Dynamic Scenes with Large-Scale Motions and Severe Saturation. IEEE Transactions on Instrumentation and Measurement, 2022, 715. DOI: 10.1109/TIM.2022.3144205.
[22] PRABHAKAR K R, AGRAWAL S, SINGH D K, et al. Towards Practical and Efficient High-Resolution HDR Deghosting with CNN // Proc of the 16th European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 497-513.
[23] CHEN J, YANG Z F, CHAN T N, et al. Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration. IEEE Transactions on Image Processing, 2022, 31: 2661-2672.
[24] CHUNG H, CHO N I. High Dynamic Range Imaging of Dynamic Scenes with Saturation Compensation but without Explicit Motion Compensation // Proc of the IEEE/CVF Winter Conference on App-lications of Computer Vision. Washington, USA: IEEE, 2022: 61-71.
[25] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale[C/OL].[2024-01-09]. https://arxiv.org/pdf/2010.11929.pdf.
[26] 薛峰,洪自坤,李书杰,等.基于Vision Transformer的中文唇语识别.模式识别与人工智能, 2022, 35(12): 1111-1121.
(XUE F, HONG Z K, LI S J, et al. Chinese Lipreading Network Based on Vision Transformer. Pattern Recognition and Artificial Intelligence, 2022, 35(12): 1111-1121.)
[27] 牛玉贞,林晓锋,许煌标,等.基于Transformer的多尺度优化低照度图像增强网络.模式识别与人工智能, 2023, 36(6): 511-529.
(NIU Y Z, LIN X F, XU H B, et al. Transformer-Based Multi-scale Optimization Network for Low-Light Image Enhancement. Pattern Recognition and Artificial Intelligence, 2023, 36(6): 511-529.)
[28] 王科平,左鑫浩,杨艺,等.基于伪全局Swin Transformer的遥感图像识别算法.模式识别与人工智能, 2023, 36(9): 818-831.
(WANG K P, ZUO X H, YANG Y, et al. Remote Sensing Image Recognition Algorithm Based on Pseudo Global Swin Transformer. Pattern Recognition and Artificial Intelligence, 2023, 36(9): 818-831.)
[29] SONG J W, PARK Y I, KONG K, et al. Selective TransHDR: Transformer-Based Selective HDR Imaging Using Ghost Region Mask // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2022: 288-304.
[30] LIU Z, WANG Y L, ZENG B, et al. Ghost-Free High Dynamic Range Imaging with Context-Aware Transformer // Proc of the European Conference on Computer Vision. Berlin, Germany: Sprin-ger, 2022: 344-360.
[31] YAN Q S, CHEN W Y, ZHANG S, et al. A Unified HDR Imaging Method with Pixel and Patch Level // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2023: 22211-22220.
[32] YAN Q S, ZHANG S, CHEN W Y, et al. SMAE: Few-Shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2023: 5775-5784.
[33] CHEN R F, ZHENG B L, ZHANG H, et al. Improving Dynamic HDR Imaging with Fusion Transformer. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(1): 340-349.
[34] TANG L F, HUANG H, ZHANG Y F, et al. Structure-Embedded Ghosting Artifact Suppression Network for High Dynamic Range Ima-ge Reconstruction. Knowledge-Based Systems, 2023, 263. DOI: 10.1016/j.knosys.2023.110278.
[35] GUO Q, SUN J Y, JUEFEI-XU F, et al. EfficientDeRain: Lear-ning Pixel-Wise Dilation Filtering for High-Efficiency Single-Image Deraining. Proceedings of the AAAI Conference on Artificial Inte-lligence, 2021, 35(2): 1487-1495.
[36] GUO Q, LI X G, JUEFEI-XU F, et al. JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting // Proc of the 29th ACM International Conference on Multimedia. New York, USA: ACM, 2021: 386-394.
[37] LI X G, GUO Q, LIN D, et al. MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 1859-1868.
[38] TURSUN O T, AKYUZ A O, ERDEM A, et al. An Objective Deghosting Quality Metric for HDR Images. Computer Graphics Forum, 2016, 35(2): 139-152.
[39] MANTIUK R, KIM K J, REMPEL A G, et al. HDR-VDP-2: A Calibrated Visual Metric for Visibility and Quality Predictions in all Luminance Conditions. ACM Transactions on Graphics, 2011, 30(4). DOI: 10.1145/2010324.1964935.
[40] GU K, WANG S Q, ZHAI G T, et al. Blind Quality Assessment of Tone-Mapped Images via Analysis of Information, Naturalness, and Structure. IEEE Transactions on Multimedia, 2016, 18(3): 432-443.
[41] FANG Y M, ZHU H W, MA K D, et al. Perceptual Evaluation for Multi-exposure Image Fusion of Dynamic Scenes. IEEE Transactions on Image Processing, 2019, 29: 1127-1138.
[42] KINGMA D P, BA J L. Adam: A Method for Stochastic Optimization[C/OL]. [2024-01-09]. https://arxiv.org/pdf/1412.6980.pdf.
[43] GOYAL P, DOLLÁR P, GIRSHICK R, et al. Accurate, Large Mini-batch SGD: Training ImageNet in 1 Hour[C/OL].[2024-01-09]. https://arxiv.org/pdf/1706.02677.pdf.
[44] NIU Y Z, WU J B, LIU W X, et al. HDR-GAN: HDR Image Reconstruction from Multi-exposed LDR Images with Large Motions. IEEE Transactions on Image Processing, 2021, 30: 3885-3896.
[45] YUAN Y, WU J Q, JING Z L, et al. Ghost-Free High Dynamic Range Imaging via Hybrid CNN-Transformer and Structure Tensor[C/OL].[2024-01-09]. https://arxiv.org/pdf/2212.00595.pdf.
[46] TEL S, WU Z W, ZHANG Y L, et al. Alignment-Free HDR Deghosting with Semantics Consistent Transformer // Proc of the IEEE/CVF International Conference on Computer Vision. Wa-shington, USA: IEEE, 2023: 12790-12799.