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XJTU Undergraduate Teaching Reform","award":["2021ZD0110700"],"award-info":[{"award-number":["2021ZD0110700"]}]},{"name":"Project of XJTU Undergraduate Teaching Reform","award":["62272375"],"award-info":[{"award-number":["62272375"]}]},{"name":"Project of XJTU Undergraduate Teaching Reform","award":["61906151"],"award-info":[{"award-number":["61906151"]}]},{"name":"Project of XJTU Undergraduate Teaching Reform","award":["62050194"],"award-info":[{"award-number":["62050194"]}]},{"name":"Project of XJTU Undergraduate Teaching Reform","award":["62037001"],"award-info":[{"award-number":["62037001"]}]},{"name":"Project of XJTU Undergraduate Teaching Reform","award":["61721002"],"award-info":[{"award-number":["61721002"]}]},{"name":"Project of XJTU Undergraduate Teaching Reform","award":["IRT_17R86"],"award-info":[{"award-number":["IRT_17R86"]}]},{"name":"Project of XJTU Undergraduate Teaching Reform","award":["IRT_17R86"],"award-info":[{"award-number":["IRT_17R86"]}]},{"name":"Project of XJTU Undergraduate Teaching Reform","award":["20JX04Y"],"award-info":[{"award-number":["20JX04Y"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We propose a new deep neural network termed TRQ3DNet which combines convolutional neural network (CNN) and transformer for hyperspectral image (HSI) denoising. The network consists of two branches. One is built by 3D quasi-recurrent blocks, including convolution and quasi-recurrent pooling operation. Specifically, the 3D convolution can extract the spatial correlation within a band, and spectral correlation between different bands, while the quasi-recurrent pooling operation is able to exploit global correlation along the spectrum. The other branch is composed of a series of Uformer blocks. The Uformer block uses window-based multi-head self-attention (W-MSA) mechanism and the locally enhanced feed-forward network (LeFF) to exploit the global and local spatial features. To fuse the features extracted by the two branches, we develop a bidirectional integration bridge (BI bridge) for better preserving the image feature information. Experimental results on synthetic and real HSI data show the superiority of our proposed network. For example, in the case of Gaussian noise with sigma 70, the PSNR value of our method significantly increases about 0.8 compared with other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs14184598","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:16:36Z","timestamp":1663197396000},"page":"4598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["TRQ3DNet: A 3D Quasi-Recurrent and Transformer Based Network for Hyperspectral Image Denoising"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2079-3354","authenticated-orcid":false,"given":"Li","family":"Pang","sequence":"first","affiliation":[{"name":"School of Automation, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weizhen","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyong","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4038","DOI":"10.1109\/TNNLS.2017.2742528","article-title":"Nonparametric coupled bayesian dictionary and classifier learning for hyperspectral classification","volume":"29","author":"Akhtar","year":"2018","journal-title":"Neural Netw. Learn. Syst. IEEE Trans."},{"key":"ref_2","first-page":"45","article-title":"Advances in hyperspectral image classification: Earth monitoring with statistical learning methods","volume":"31","author":"Tuia","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1109\/TNNLS.2013.2293061","article-title":"Jointly learning the hybrid crf and mlr model for simultaneous denoising and classification of hyperspectral imagery","volume":"25","author":"Zhong","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TNNLS.2015.2477537","article-title":"Salient band selection for hyperspectral image classification via manifold ranking","volume":"27","author":"Wang","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TNNLS.2018.2841009","article-title":"Self-paced learning-based probability subspace projection for hyperspectral image classification","volume":"30","author":"Yang","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.neucom.2014.01.068","article-title":"Spatially regularized semisupervised ensembles of extreme learning machines for hyperspectral image segmentation","volume":"149","author":"Ayerdi","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_7","unstructured":"Noyel, G., Angulo, J., and Jeulin, D. (2016). On distances, paths and connections for hyperspectral image segmentation. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5067","DOI":"10.1109\/TGRS.2015.2417162","article-title":"Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rasti, B., Chang, Y., Dalsasso, E., Denis, L., and Ghamisi, P. (2021). Image restoration for remote sensing: Overview and toolbox. arXiv.","DOI":"10.1109\/MGRS.2021.3121761"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Peng, Y., Meng, D., Xu, Z., Gao, C., Yang, Y., and Zhang, B. (2014, January 23\u201328). Decomposable nonlocal tensor dictionary learning for multispectral image denoising. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.377"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TIP.2012.2210725","article-title":"Nonlocal transform-domain filter for volumetric data denoising and reconstruction","volume":"22","author":"Maggioni","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xie, Q., Zhao, Q., Meng, D., Xu, Z., Gu, S., Zuo, W., and Zhang, L. (2016, January 27\u201330). Multispectral images denoising by intrinsic tensor sparsity regularization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.187"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chang, Y., Yan, L., and Zhong, S. (2017, January 21\u201326). Hyper-laplacian regularized unidirectional low-rank tensor recovery for multispectral image denoising. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.625"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2013.2284280","article-title":"Hyperspectral image restoration using low-rank matrix recovery","volume":"52","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TGRS.2015.2452812","article-title":"Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration","volume":"54","author":"He","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1109\/TCYB.2017.2677944","article-title":"Denoising hyperspectral image with non-iid noise structure","volume":"48","author":"Chen","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/JSTARS.2017.2779539","article-title":"Hyperspectral image restoration via total variation regularized low-rank tensor decomposition","volume":"11","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image denoising by sparse 3-d transform-domain collaborative filtering","volume":"16","author":"Dabov","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/TGRS.2018.2859203","article-title":"Hsi-denet: Hyperspectral image restoration via convolutional neural network","volume":"57","author":"Chang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2018.2865197","article-title":"Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network","volume":"57","author":"Yuan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., and Xu, C. (2017, January 22\u201329). Memnet: A persistent memory network for image restoration. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.486"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1109\/TNNLS.2020.2978756","article-title":"3-d quasi-recurrent neural network for hyperspectral image denoising","volume":"32","author":"Wei","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","first-page":"1","article-title":"Deep spatial-spectral global reasoning network for hyperspectral image denoising","volume":"60","author":"Cao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Heo, B., Yun, S., Han, D., Chun, S., Choe, J., and Oh, S.J. (2021, January 11\u201317). Rethinking spatial dimensions of vision transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.01172"},{"key":"ref_27","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., and Houlsby, N. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., and Shlens, J. (2021, January 11\u201317). Scaling local self-attention for parameter efficient visual backbones. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01270"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Maji, B., and Swain, M. (2022). Advanced fusion-based speech emotion recognition system using a dual-attention mechanism with conv-caps and bi-gru features. Electronics, 11.","DOI":"10.3390\/electronics11091328"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., and Yan, S. (2022, January 21\u201324). Metaformer is actually what you need for vision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"ref_31","first-page":"9355","article-title":"Twins: Revisiting the design of spatial attention in vision transformers","volume":"34","author":"Chu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_33","first-page":"14745","article-title":"Transgan: Two pure transformers can make one strong gan, and that can scale up","volume":"34","author":"Jiang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","first-page":"18367","article-title":"Improved transformer for high-resolution gans","volume":"34","author":"Zhao","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","unstructured":"Xu, R., Xu, X., Chen, K., Zhou, B., and Chen, C.L. (2021). Stransgan: An empirical study on transformer in gans. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L.V., and Timofte, R. (2021, January 11\u201317). Swinir: Image restoration using swin transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_37","unstructured":"Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., and Li, H. (2020, January 14\u201319). Uformer: A general u-shaped transformer for image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_38","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3d convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning spatiotemporal features with 3d convolutional networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_41","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., and Vaswani, A. (2018). Self-attention with relative position representations. arXiv.","DOI":"10.18653\/v1\/N18-2074"},{"key":"ref_44","unstructured":"Hendrycks, D., and Gimpel, K. (2022, September 08). Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units. Available online: https:\/\/openreview.net\/forum?id=Bk0MRI5lg."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Sparse recovery of hyperspectral signal from natural rgb images. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46466-4"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Park, J.I., Lee, M.H., Grossberg, M.D., and Nayar, S.K. (2007, January 14\u201321). Multispectral imaging using multiplexed illumination. Proceedings of the IEEE International Conference on Computer Vision, Rio De Janeiro, Brazi.","DOI":"10.1109\/ICCV.2007.4409090"},{"key":"ref_47","unstructured":"Gamba, P. (2004, January 20\u201324). A collection of data for urban area characterization. Proceedings of the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mnih, V., and Hinton, G.E. (2010). Learning to detect roads in high-resolution aerial images. European Cnference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Landgrebe, D.A. (2003). Signal Theory Methods in Multispectral Remote Sensing, John Wiley & Sons.","DOI":"10.1002\/0471723800"},{"key":"ref_50","unstructured":"Kingma, D., and Ba, J. (2014, January 14\u201316). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_52","unstructured":"Yuhas, R.H., Boardman, J.W., and Goetz, A.F.H. (1993). Determination of Semi-Arid Landscape Endmembers and Seasonal Trends Using Convex Geometry Spectral Unmixing Techniques, NTRS."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liu, X., Tanaka, M., and Okutomi, M. (2013, January 15\u201318). Noise level estimation using weak textured patches of a single noisy image. Proceedings of the IEEE International Conference on Image Processing, Melbourne, Australia.","DOI":"10.1109\/ICIP.2012.6466947"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Peng, Z., Huang, W., Gu, S., Xie, L., Wang, Y., Jiao, J., and Ye, Q. (2021, January 10\u201317). Conformer: Local features coupling global representations for visual recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00042"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep supervised learning for hyperspectral data classification through convolutional neural networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4598\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:31:40Z","timestamp":1760142700000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4598"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":56,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184598"],"URL":"https:\/\/doi.org\/10.3390\/rs14184598","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,14]]}}}