{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:35:51Z","timestamp":1760232951266,"version":"build-2065373602"},"reference-count":65,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Provincial Natural Science Foundation","award":["ZR2019BF022","62001272"],"award-info":[{"award-number":["ZR2019BF022","62001272"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2019BF022","62001272"],"award-info":[{"award-number":["ZR2019BF022","62001272"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Images captured in bad weather are not conducive to visual tasks. Rain streaks in rainy images will significantly affect the regular operation of imaging equipment; to solve this problem, using multiple neural networks is a trend. The ingenious integration of network structures allows for full use of the powerful representation and fitting abilities of deep learning to complete low-level visual tasks. In this study, we propose a generative adversarial network (GAN) with multiple attention mechanisms for image rain removal tasks. Firstly, to the best of our knowledge, we propose a pretrained vision transformer (ViT) as the discriminator in GAN for single-image rain removal for the first time. Secondly, we propose a neural network training method that can use a small amount of data for training while maintaining promising results and reliable visual quality. A large number of experiments prove the correctness and effectiveness of our method. Our proposed method achieves better results on synthetic and real image datasets than multiple state-of-the-art methods, even when using less training data.<\/jats:p>","DOI":"10.3390\/s22249587","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T05:50:52Z","timestamp":1670392252000},"page":"9587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PRAGAN: Progressive Recurrent Attention GAN with Pretrained ViT Discriminator for Single-Image Deraining"],"prefix":"10.3390","volume":"22","author":[{"given":"Bingcai","family":"Wei","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4300-1789","authenticated-orcid":false,"given":"Liye","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"ref_1","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. (2017). Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_3","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_4","unstructured":"Zhang, X., Luo, H., Fan, X., Xiang, W., Sun, Y., Xiao, Q., and Sun, J. (2017). Alignedreid: Surpassing Human-Level Performance in Person Re-Identification. arXiv."},{"key":"ref_5","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_12","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_13","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., and Houlsby, N. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, K., Luo, W., Zhong, Y., Ma, L., Stenger, B., Liu, W., and Li, H. (2020, January 14\u201319). Deblurring by Realistic Blurring. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00281"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., and Ukita, N. (2019, January 16\u201320). Recurrent Back-Projection Network for Video Super-Resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00402"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tassano, M., Delon, J., and Veit, T. (2019, January 22\u201325). DVDNET: A Fast Network for Deep Video Denoising. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803136"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TIP.2017.2691802","article-title":"Clearing the skies: A Deep Network Architecture for Single-Image Rain Removal","volume":"26","author":"Fu","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., and Yan, S. (2017, January 21\u201326). Deep Joint Rain Detection and Removal from a Single Image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.183"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Patel, V.M. (2018, January 18\u201322). Density-Aware Single Image De-Raining Using a Multi-Stream Dense Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, CA, USA.","DOI":"10.1109\/CVPR.2018.00079"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1109\/TCSVT.2019.2920407","article-title":"Image De-Raining Using a Conditional Generative Adversarial Network","volume":"30","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ren, D., Zuo, W., Hu, Q., Zhu, P., and Meng, D. (2019, January 16\u201320). Progressive Image Deraining Networks: A Better and Simpler Baseline. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00406"},{"key":"ref_23","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long Short-Term memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_25","unstructured":"Azad, R., Asadi-Aghbolaghi, M., Fathy, M., and Escalera, S. (November, January 27). Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_27","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017). Improved training of wasserstein gans. Adv. Neural Inf. Processing Syst., 30."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., van Gool, L., and Timofte, R. (2021, January 10\u201317). Swinir: Image Restoration Using Swin Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_30","unstructured":"Park, N., and Kim, S. (2022). How Do Vision Transformers Work?. arXiv."},{"key":"ref_31","first-page":"87","article-title":"A Survey on Vision Transformer","volume":"1","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer Normalization. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss Functions for Image Restoration with Neural Networks","volume":"3","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Shen, X., Wang, J., and Jia, J. (2018, January 18\u201322). Scale-Recurrent Network for Deep Image Deblurring. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, CA, USA.","DOI":"10.1109\/CVPR.2018.00853"},{"key":"ref_35","unstructured":"Nah, S., Kim, T.H., and Lee, K.M. (July, January USA). Deep Multi-Scale Convolutional Neural Network for Dynamic Scene Deblurring. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jiang, K., Wang, Z., Yi, P., Chen, C., Huang, B., Luo, Y., and Jiang, J. (2020, January 14\u201319). Multi-Scale Progressive Fusion Network for Single Image deraining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00837"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gao, H., Tao, X., Shen, X., and Jia, J. (2019, January 16\u201320). Dynamic Scene Deblurring with Parameter Selective Sharing and Nested Skip Connections. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00397"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1109\/83.791975","article-title":"Optimally Isotropic Laplacian Operator","volume":"8","author":"Rosenfeld","year":"1999","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s41095-021-0247-3","article-title":"Transformers in computational visual media: A survey","volume":"8","author":"Xu","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_41","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","article-title":"GPT-3: Its nature, scope, limits, and consequences","volume":"30","author":"Floridi","year":"2020","journal-title":"Minds Mach."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., and Girshick, R. (2022, January 19\u201324). Masked Autoencoders are Scalable Vision Learners. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_46","unstructured":"He, K., Girshick, R., and Doll\u00e1r, P. (November, January 27). Rethinking Imagenet Pre-Training. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6142","DOI":"10.1109\/TIP.2021.3092814","article-title":"Structure-Aware Motion Deblurring Using Multi-Adversarial Optimized Cyclegan","volume":"30","author":"Wen","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., and Matas, J. (2018, January 18\u201322). Deblurgan: Blind Motion Deblurring Using Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00854"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cho, S.J., Ji, S.W., Hong, J.P., Jung, S.W., and Ko, S.J. (2021, January 10\u201317). Rethinking Coarse-to-Fine Approach in Single Image Deblurring. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00460"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Park, D., Kang, D.U., Kim, J., and Chun, S.Y. (2020, January 23\u201328). Multi-Temporal Recurrent Neural Networks for Progressive Non-Uniform Single Image Deblurring with Incremental Temporal Training. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58539-6_20"},{"key":"ref_52","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_53","unstructured":"Loshchilov, I., and Hutter, F. (2016). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv."},{"key":"ref_54","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled Weight Decay Regularization. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, J., Lin, Z., Liu, H., and Zha, H. (2018, January 8\u201314). Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_16"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yasarla, R., and Patel, V.M. (2019, January 16\u201320). Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-raining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00860"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wei, W., Meng, D., Zhao, Q., Xu, Z., and Wu, Y. (2019, January 16\u201320). Semi-Supervised Transfer Learning for Image Rain Removal. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00400"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., and Paisley, J. (2017, January 21\u201326). Removing Rain from Single Images via a Deep Detail Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.186"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., and Shao, L. (2021, January 19\u201325). Multi-Stage Progressive Image Restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"ref_60","unstructured":"Li, Y., Tan, R.T., Guo, X., Lu, J., and Brown, M.S. (July, January 26). Rain Streak Removal Using Layer Priors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_61","unstructured":"Wang, H., Wu, Y., Li, M., Zhao, Q., and Meng, D. (2019). A Survey on Rain Removal from Video and Single Image. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, T., Yang, X., Xu, K., Chen, S., Zhang, Q., and Lau, R.W. (2019, January 16\u201320). Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01255"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Luo, Y., Xu, Y., and Ji, H. (2015, January 7\u201313). Removing Rain from a Single Image via Discriminative Sparse Coding. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.388"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Gu, S., Meng, D., Zuo, W., and Zhang, L. (2017, January 22\u201329). Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.189"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/TPAMI.2019.2895793","article-title":"Joint Rain Detection and Removal from a Single Image with Contextualized Deep Networks","volume":"42","author":"Yang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. 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