{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:05:46Z","timestamp":1770998746997,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T00:00:00Z","timestamp":1599436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting artifacts in the presence of large object motion. Flow-based methods face the same issue since their optical flow algorithms yield huge alignment errors. To eliminate ghosting artifacts, we propose a simple yet effective alignment network for solving the misalignment. The proposed pyramid inter-attention module (PIAM) performs alignment of LDR features by leveraging inter-attention maps. Additionally, to boost the representation of aligned features in the merging process, we propose a dual excitation block (DEB) that recalibrates each feature both spatially and channel-wise. Exhaustive experimental results demonstrate the effectiveness of the proposed PIAM and DEB, achieving state-of-the-art performance in terms of producing ghost-free HDR images.<\/jats:p>","DOI":"10.3390\/s20185102","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:11:51Z","timestamp":1599523911000},"page":"5102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Pyramid Inter-Attention for High Dynamic Range Imaging"],"prefix":"10.3390","volume":"20","author":[{"given":"Sungil","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaehoon","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wonil","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihwan","family":"Choe","sequence":"additional","affiliation":[{"name":"Samsung Electronics, Suwon 16677, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jisung","family":"Yoo","sequence":"additional","affiliation":[{"name":"Samsung Electronics, Suwon 16677, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwanghoon","family":"Sohn","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Banterle, F., Artusi, A., Debattista, K., and Chalmers, A. (2017). Advanced High Dynamic Range Imaging, CRC Press.","DOI":"10.1201\/9781315119526"},{"key":"ref_2","unstructured":"Mann, S., and Rosalind, W. (1995, January 7\u201311). On being undigital with digital cameras: Extending dynamic range by combining exposed pictures. Proceedings of the IST 48th Annual Conference Society for Imaging Science and Technology, Cambridge, MA, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Debevec, P.E., and Malik, J. (2008). Recovering high dynamic range radiance maps from photographs. ACM SIGGRAPH, 1\u201310.","DOI":"10.1145\/1401132.1401174"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1109\/TIP.2011.2170079","article-title":"Gradient-directed multiexposure composition","volume":"21","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MCG.2008.23","article-title":"Automatic high-dynamic range image generation for dynamic scenes","volume":"28","author":"Jacobs","year":"2008","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_6","first-page":"139","article-title":"Fast and robust high dynamic range image generation with camera and object movement","volume":"277284","author":"Grosch","year":"2006","journal-title":"Vision, Model. Vis. Rwth Aachen"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pece, F., and Kautz, J. (2010, January 17\u201318). Bitmap movement detection: HDR for dynamic scenes. Proceedings of the 2010 Conference on Visual Media Production, London, UK.","DOI":"10.1109\/CVMP.2010.8"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Heo, Y.S., Lee, K.M., Lee, S.U., Moon, Y., and Cha, J. (2010, January 8\u201312). Ghost-free high dynamic range imaging. Proceedings of the Asian Conference on Computer Vision, Queenstown, New Zealand.","DOI":"10.1007\/978-3-642-19282-1_39"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1145\/882262.882270","article-title":"High dynamic range video","volume":"22","author":"Kang","year":"2003","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bogoni, L. (2000, January 3\u20137). Extending dynamic range of monochrome and color images through fusion. Proceedings of the 15th International Conference on Pattern Recognition (ICPR-2000), Barcelona, Spain.","DOI":"10.1109\/ICPR.2000.903475"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1145\/2366145.2366222","article-title":"Robust patch-based hdr reconstruction of dynamic scenes","volume":"31","author":"Sen","year":"2012","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hafner, D., Demetz, O., and Weickert, J. (2014, January 24\u201328). Simultaneous HDR and optic flow computation. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.360"},{"key":"ref_13","unstructured":"Tomaszewska, A., and Mantiuk, R. (February, January 29). Image registration for multi-exposure high dynamic range image acquisition. Proceedings of the 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2007 in co-operation with EUROGRAPHICS, Prague, Czech Republic."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gallo, O., Troccoli, A., Hu, J., Pulli, K., and Kautz, J. (2015, January 8\u201310). Locally non-rigid registration for mobile HDR photography. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301366"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hu, J., Gallo, O., Pulli, K., and Sun, X. (2013, January 23\u201328). HDR deghosting: How to deal with saturation?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.154"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1111\/j.1467-8659.2011.01870.x","article-title":"Freehand HDR imaging of moving scenes with simultaneous resolution enhancement","volume":"30","author":"Zimmer","year":"2011","journal-title":"Comput. Graph. Forum"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1145\/3130800.3130834","article-title":"Deep reverse tone mapping","volume":"36","author":"Endo","year":"2017","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3845","DOI":"10.1109\/TIP.2020.2966075","article-title":"Unsupervised Deep Image Fusion With Structure Tensor Representations","volume":"29","author":"Jung","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1145\/3072959.3073609","article-title":"Deep high dynamic range imaging of dynamic scenes","volume":"36","author":"Kalantari","year":"2017","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wu, S., Xu, J., Tai, Y.W., and Tang, C.K. (2018, January 8\u201314). Deep high dynamic range imaging with large foreground motions. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01216-8_8"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yan, Q., Gong, D., Shi, Q., Hengel, A.V.D., Shen, C., Reid, I., and Zhang, Y. (2019, January 16\u201320). Attention-guided Network for Ghost-free High Dynamic Range Imaging. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00185"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3130800.3130816","article-title":"HDR image reconstruction from a single exposure using deep CNNs","volume":"36","author":"Eilertsen","year":"2017","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_23","unstructured":"Liu, C. (2009). Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/cgf.12818","article-title":"An objective deghosting quality metric for HDR images","volume":"35","author":"Tursun","year":"2016","journal-title":"Comput. Graph. Forum"},{"key":"ref_25","first-page":"65010E","article-title":"High-dynamic-range video for photometric measurement of illumination","volume":"Volume 6501","author":"Unger","year":"2007","journal-title":"Sensors, Cameras, and Systems for Scientific\/Industrial Applications VIII"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khan, E.A., Akyuz, A.O., and Reinhard, E. (2006, January 8\u201311). Ghost removal in high dynamic range images. Proceedings of the 2006 International Conference on Image Processing, Atlanta, GA, USA.","DOI":"10.1109\/ICIP.2006.312892"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1109\/LSP.2014.2323404","article-title":"Ghost-free high dynamic range imaging via rank minimization","volume":"21","author":"Lee","year":"2014","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1109\/TPAMI.2014.2361338","article-title":"Robust high dynamic range imaging by rank minimization","volume":"37","author":"Oh","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jinno, T., and Okuda, M. (2008, January 12\u201315). Motion blur free HDR image acquisition using multiple exposures. Proceedings of the 2008 15th IEEE International Conference on Image Processing, San Diego, CA, USA.","DOI":"10.1109\/ICIP.2008.4712002"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., and Brox, T. (2015, January 7\u201313). Flownet: Learning optical flow with convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.316"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Smagt, P.V.D., Cremers, D., and Brox, T. (2017, January 22\u201325). Flownet 2.0: Evolution of optical flow estimation with deep networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., and Kautz, J. (2018, January 19\u201321). Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00931"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 19\u201321). Non-local neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_36","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9\u201315). Self-attention generative adversarial networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, G., He, X., Zhang, W., Chang, H., Dong, L., and Lin, L. (2018, January 12\u201316). Non-locally enhanced encoder-decoder network for single image de-raining. Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Korea.","DOI":"10.1145\/3240508.3240636"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, Y., Liang, Z., Lin, Z., Yang, J., An, W., and Guo, Y. (2019, January 16\u201320). Learning parallax attention for stereo image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01253"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chang, J.R., and Chen, Y.S. (2018, January 19\u201321). Pyramid stereo matching network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake, UT, USA.","DOI":"10.1109\/CVPR.2018.00567"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., and Bry, A. (2017, January 22\u201329). End-to-end learning of geometry and context for deep stereo regression. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.17"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liang, Z., Feng, Y., Guo, Y., Liu, H., Chen, W., Qiao, L., and Zhang, J. (2018, January 19\u201321). Learning for disparity estimation through feature constancy. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00297"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, Y., Yang, Z., Zhao, L., Wang, P., and Xu, W. (2018, January 19\u201321). Occlusion aware unsupervised learning of optical flow. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00513"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., and Brostow, G.J. (2017, January 22\u201325). Unsupervised monocular depth estimation with left-right consistency. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.699"},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference for Learning Representations, San Diego, CA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2010324.1964935","article-title":"HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions","volume":"30","author":"Mantiuk","year":"2011","journal-title":"ACM Trans. Graph. 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