{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:51:12Z","timestamp":1760233872034,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:00:00Z","timestamp":1614902400000},"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>In order to remove the strong noise with complex shapes and high density in nuclear radiation scenes, a lightweight network composed of a Noise Learning Unit (NLU) and Texture Learning Unit (TLU) was designed. The NLU is bilinearly composed of a Multi-scale Kernel Module (MKM) and a Residual Module (RM), which learn non-local information and high-level features, respectively. Both the MKM and RM have receptive field blocks and attention blocks to enlarge receptive fields and enhance features. The TLU is at the bottom of the NLU and learns textures through an independent loss. The entire network adopts a Mish activation function and asymmetric convolutions to improve the overall performance. Compared with 12 denoising methods on our nuclear radiation dataset, the proposed method has the fewest model parameters, the highest quantitative metrics, and the best perceptual satisfaction, indicating its high denoising efficiency and rich texture retention.<\/jats:p>","DOI":"10.3390\/s21051810","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T05:03:15Z","timestamp":1614920595000},"page":"1810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Lightweight Image Restoration Network for Strong Noise Removal in Nuclear Radiation Scenes"],"prefix":"10.3390","volume":"21","author":[{"given":"Xin","family":"Sun","sequence":"first","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China"}]},{"given":"Hongwei","family":"Luo","sequence":"additional","affiliation":[{"name":"Shenzhen Launch Digital Technology Co., Ltd., Shenzhen 518000, China"}]},{"given":"Guihua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China"}]},{"given":"Chunmei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China"}]},{"given":"Feng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"key":"ref_1","first-page":"13","article-title":"The experimental research of the performance degradation in PDD CMOS image sensors induced by total ionizing dose radiation effects","volume":"5","author":"Wang","year":"2017","journal-title":"Prog. Rep. China Nucl. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Las Condes, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep learning for generic object detection: A survey","volume":"128","author":"Liu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., and Terzopoulos, D. (2020). Image segmentation using deep learning: A survey. arXiv.","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"ref_5","unstructured":"Kang, Y. (2019). Research on Positioning Technology of Visual Simultaneous Localization and Mapping in Nuclear. [Master\u2019s Thesis, Southwest University of Science and Technology]."},{"key":"ref_6","first-page":"59","article-title":"A new image denoising method for monitoring in intense radioactive environment","volume":"30","author":"Wang","year":"2011","journal-title":"Transducer Microsyst. Technol."},{"key":"ref_7","unstructured":"Zhang, L.Y., Li, W.S., Chen, W.J., and Chi, Z.Y. (2015, January 18). Image Denoising in the nuclear radiation environment. Proceedings of the On Optical Fiber Communication and Integrated Optics, Nanjing, Jiangsu, China."},{"key":"ref_8","first-page":"56","article-title":"A Novel Anti-nuclear Radiation Image Restoration Algorithm Based on Inpainting Technology","volume":"30","author":"Yang","year":"2016","journal-title":"J. Univ. South China"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., and Lin, C.-W. (2019). Deep learning on image denoising: An overview. arXiv.","DOI":"10.1016\/j.neunet.2020.07.025"},{"key":"ref_10","unstructured":"Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., and Aila, T. (2018). Noise2noise: Learning image restoration without clean data. arXiv."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1109\/TIP.2012.2235847","article-title":"Nonlocally centralized sparse representation for image restoration","volume":"22","author":"Dong","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"023016","DOI":"10.1117\/1.3600632","article-title":"Color demosaicking by local directional interpolation and nonlocal adaptive thresholding","volume":"20","author":"Zhang","year":"2011","journal-title":"J. Electron. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fang, J., Hu, S.H., and Ma, X.L. (2018). A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation. Sensors, 18.","DOI":"10.3390\/s18103448"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., and Feng, X. (2015, January 7\u201312). Weighted nuclear norm minimization with application to image denoising. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2014.366"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","article-title":"FFDNet: Toward a fast and flexible solution for CNN-based image denoising","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., and Zhang, L. (2019, January 16\u201320). Toward convolutional blind denoising of real photographs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00181"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.neunet.2019.08.022","article-title":"Image denoising using deep CNN with batch renormalization","volume":"121","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neunet.2019.12.024","article-title":"Attention-guided CNN for image denoising","volume":"124","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102774","DOI":"10.1016\/j.jvcir.2020.102774","article-title":"Detail retaining convolutional neural network for image denoising","volume":"71","author":"Li","year":"2020","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_22","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2015, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-first AAAI Conference on Artificial Intelligence, San Francisco, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Martinez, R.P., Schiopu, I., Cornelis, B., and Munteanu, A. (2021). Real-Time Instance Segmentation of Traffic Videos for Embedded Devices. Sensors, 21.","DOI":"10.3390\/s21010275"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, S., and Huang, D. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_25","unstructured":"Jaderberg, M., Simonyan, K., and Zisserman, A. (2017, January 4\u20139). Spatial transformer networks. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and So Kweon, I. (2017, January 23\u201328). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Venice, Italy.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sun, Y.C., Gao, W., Pan, S.G., Zhao, T., and Peng, Y.H. (2021). An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms. Appl. Sci., 11.","DOI":"10.3390\/app11030968"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Park, S.Y., and Heo, Y.S. (2020). Knowledge Distillation for Semantic Segmentation Using Channel and Spatial Correlations and Adaptive Cross Entropy. Sensors, 20.","DOI":"10.3390\/s20164616"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","article-title":"Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss","volume":"37","author":"Yang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gholizadeh-Ansari, M., Alirezaie, J., and Babyn, P. (2018, January 17\u201321). Low-dose CT denoising with dilated residual network. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513453"},{"key":"ref_33","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). Enet: A deep neural network architecture for real-time texture segmentation. arXiv."},{"key":"ref_34","unstructured":"Misra, D. (2019). Mish: A self regularized non-monotonic neural activation function. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ding, X., Guo, Y., Ding, G., and Han, J. (2019, January 27\u201328). Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00200"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TIP.2016.2631888","article-title":"Waterloo exploration database: New challenges for image quality assessment models","volume":"26","author":"Ma","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","unstructured":"Franzen, R. (2021, January 01). Kodak Lossless True Color Image Suite. Available online: http:\/\/r0k.us\/graphics\/kodak4."},{"key":"ref_38","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hore, A., and Ziou, D. (2010, January 23\u201326). Image quality metrics: PSNR vs. SSIM. Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_40","unstructured":"Xu, J., Li, H., Liang, Z., Zhang, D., and Zhang, L. (2018). Real-world noisy image denoising: A new benchmark. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1810\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:33:24Z","timestamp":1760160804000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1810"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,5]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21051810"],"URL":"https:\/\/doi.org\/10.3390\/s21051810","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,3,5]]}}}