{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T01:19:12Z","timestamp":1769822352209,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T00:00:00Z","timestamp":1625788800000},"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>Due to the blur information and content information entanglement in the blind deblurring task, it is very challenging to directly recover the sharp latent image from the blurred image. Considering that in the high-dimensional feature map, blur information mainly exists in the low-frequency region, and content information exists in the high-frequency region. In this paper, we propose a encoder\u2013decoder model to realize disentanglement from the perspective of frequency, and we named it as frequency disentanglement distillation image deblurring network (FDDN). First, we modified the traditional distillation block by embedding the frequency split block (FSB) in the distillation block to separate the low-frequency and high-frequency region. Second, the modified distillation block, we named frequency distillation block (FDB), can recursively distill the low-frequency feature to disentangle the blurry information from the content information, so as to improve the restored image quality. Furthermore, to reduce the complexity of the network and ensure the high-dimension of the feature map, the frequency distillation block (FDB) is placed on the end of encoder to edit the feature map on the latent space. Quantitative and qualitative experimental evaluations indicate that the FDDN can remove the blur effect and improve the image quality of actual and simulated images.<\/jats:p>","DOI":"10.3390\/s21144702","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T10:50:38Z","timestamp":1625827838000},"page":"4702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Frequency Disentanglement Distillation Image Deblurring Network"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0387-6329","authenticated-orcid":false,"given":"Yiming","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Physical Education, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4088-7473","authenticated-orcid":false,"given":"Hualing","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0411-1539","authenticated-orcid":false,"given":"Mengzi","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dahong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1109\/TPAMI.2010.222","article-title":"Richardson-Lucy Deblurring for Scenes under a Projective Motion Path","volume":"33","author":"Tai","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nah, S., Kim, T.H., and Lee, K.M. (2016). Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring. arXiv.","DOI":"10.1109\/CVPR.2017.35"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, J., Pan, J., Ren, J., Song, Y., and Yang, M.H. (2018, January 18\u201323). Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00267"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aittala, M., and Durand, F. (2018, January 8\u201314). Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_45"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., and Matas, J. (2018, January 18\u201323). DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00854"},{"key":"ref_6","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Bing, X., and Bengio, Y. (2014). Generative Adversarial Nets, MIT Press."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zoran, D., and Weiss, Y. (2011, January 6\u201313). From learning models of natural image patches to whole image restoration. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126278"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dong, G., Jie, Y., Liu, L., Zhang, Y., and Shi, Q. (2017, January 21\u201326). From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.405"},{"key":"ref_9","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., and Wang, Z. (2017, January 21\u201326). DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better. Proceedings of the 2017 IEEE\/CVF International Conference on Computer Vision (ICCV), Honolulu, HI, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, L., Fang, F., Wang, T., and Zhang, G. (2019, January 15\u201320). Blind Image Deblurring With Local Maximum Gradient Prior. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00184"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1007\/s11263-018-01146-0","article-title":"Blind Image Deblurring via Deep Discriminative Priors","volume":"127","author":"Li","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pan, J., Sun, D., Pfister, H., and Yang, M.H. (2016, January 27\u201330). Blind Image Deblurring Using Dark Channel Prior. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.180"},{"key":"ref_14","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., and Woo, W. (2015, January 7\u201312). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, H., Xu, B., Yan, Z., Kalantidis, Y., Rohrbach, M., Yan, S., and Feng, J. (November, January 27). Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00353"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dai, Y., Li, H., and Koniusz, P. (2019, January 15\u201320). Deep Stacked Hierarchical Multi-patch Network for Image Deblurring. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00613"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gao, H., Tao, X., Shen, X., and Jia, J. (2019, January 15\u201320). Dynamic Scene Deblurring with Parameter Selective Sharing and Nested Skip Connections. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00397"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lu, B., Chen, J.C., and Chellappa, R. (2019, January 15\u201320). Unsupervised Domain-Specific Deblurring via Disentangled Representations. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01047"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, B., and Yin, H. (2021). Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution. arXiv.","DOI":"10.1109\/IJCNN52387.2021.9534213"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"76707","DOI":"10.1109\/ACCESS.2021.3082211","article-title":"A Two-Stage Network for Image Deblurring","volume":"9","author":"Pan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45853","DOI":"10.1109\/ACCESS.2021.3067055","article-title":"Two-Level Wavelet-Based Convolutional Neural Network for Image Deblurring","volume":"9","author":"Wu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Feng, H., Guo, J., Xu, H., and Ge, S.S. (2021). SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks. Sensors, 21.","DOI":"10.3390\/s21113641"},{"key":"ref_23","unstructured":"Wang, H., Yue, Z., Zhao, Q., and Meng, D. (2021). A Deep Variational Bayesian Framework for Blind Image Deblurring. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S.H., Hayat, M., Khan, F.S., Yang, M., and Shao, L. (2021). Multi-Stage Progressive Image Restoration. arXiv.","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_26","first-page":"41","article-title":"Residual Feature Distillation Network for Lightweight Image Super-Resolution","volume":"Volume 12537","author":"Bartoli","year":"2020","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2020 Workshops"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., and Wang, Z. (2016, January 27\u201330). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_28","unstructured":"Amsaleg, L., Huet, B., Larson, M.A., Gravier, G., Hung, H., Ngo, C., and Ooi, W.T. (2019, January 21\u201325). Lightweight Image Super-Resolution with Information Multi-distillation Network. Proceedings of the 27th ACM International Conference on Multimedia, MM 2019, Nice, France."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6885","DOI":"10.1109\/TIP.2020.2995048","article-title":"Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring","volume":"29","author":"Cai","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shen, Z., Wang, W., Lu, X., Shen, J., Ling, H., Xu, T., and Shao, L. (November, January 27). Human-Aware Motion Deblurring. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00567"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Wang, Y., Shen, X., Wang, J., and Jia, J. (2018, January 18\u201323). Scale-recurrent Network for Deep Image Deblurring. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00853"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"188587","DOI":"10.1109\/ACCESS.2020.3028157","article-title":"Semantic Information Supplementary Pyramid Network for Dynamic Scene Deblurring","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Su, W., and Ma, D. (2020, January 13\u201319). Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00361"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, J., Cao, W., Xu, Z., and Ponce, J. (2015, January 7\u201312). Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298677"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4702\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:28:22Z","timestamp":1760164102000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,9]]},"references-count":35,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144702"],"URL":"https:\/\/doi.org\/10.3390\/s21144702","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,9]]}}}