{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:49:16Z","timestamp":1774540156898,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Basic Research Plan in Shannxi Province of China","award":["2023-JQ-QC-0714"],"award-info":[{"award-number":["2023-JQ-QC-0714"]}]},{"name":"National Science Basic Research Plan in Shannxi Province of China","award":["S24-025-III"],"award-info":[{"award-number":["S24-025-III"]}]},{"name":"Photon Plan in Xi\u2019an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences","award":["2023-JQ-QC-0714"],"award-info":[{"award-number":["2023-JQ-QC-0714"]}]},{"name":"Photon Plan in Xi\u2019an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences","award":["S24-025-III"],"award-info":[{"award-number":["S24-025-III"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating the precise modeling of real noise distributions while neglecting improvements in the noise modeling capabilities of learning models. To address this situation, a novel self-adaptive deformable-convolution-based U-Net (SD-UNet) model is proposed in this paper. Firstly, deformable convolution is employed to tackle noise patterns with different geometries, thus extracting more reliable noise representations. After that, a self-adaptive learning block is proposed to enable the network to automatically select appropriate learning branches for noise with different scales. Finally, a novel structural loss function is leveraged to evaluate the difference between denoised and clean images. The experimental results on multiple public datasets validate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/sym16060646","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T09:58:47Z","timestamp":1716458327000},"page":"646","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2525-5479","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"first","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianzhong","family":"Cao","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huinan","family":"Guo","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3915-5451","authenticated-orcid":false,"given":"Cheng","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2980179.2980254","article-title":"Burst photography for high dynamic range and low-light imaging on mobile cameras","volume":"35","author":"Hasinoff","year":"2016","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1145\/3355089.3356508","article-title":"Handheld mobile photography in very low light","volume":"38","author":"Liba","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guerrieri, F., Tisa, S., and Zappa, F. (2009, January 20\u201322). Fast single-photon imager acquires 1024 pixels at 100 kframe\/s. Proceedings of the Sensors, Cameras, and Systems for Industrial\/Scientific Applications X, San Jose, CA, USA.","DOI":"10.1117\/12.807426"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5913","DOI":"10.1038\/ncomms6913","article-title":"Imaging with a small number of photons","volume":"6","author":"Morris","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, L., Zhang, D., and Feng, X. (2017, January 22\u201329). Multi-channel weighted nuclear norm minimization for real color image denoising. Proceedings of the IEEE international Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.125"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, L., and Zhang, D. (2018, January 8\u201314). A trilateral weighted sparse coding scheme for real-world image denoising. Proceedings of the European conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_2"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., and Zhang, L. (2019, January 15\u201320). Toward convolutional blind denoising of real photographs. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00181"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Brooks, T., Mildenhall, B., Xue, T., Chen, J., Sharlet, D., and Barron, J.T. (2019, January 15\u201320). Unprocessing images for learned raw denoising. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01129"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1109\/TIP.2008.2001399","article-title":"Practical Poissonian\u2013Gaussian noise modeling and fitting for single-image raw-data","volume":"17","author":"Foi","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_12","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TIP.2023.3243853","article-title":"Single-source domain expansion network for cross-scene hyperspectral image classification","volume":"32","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"120496","DOI":"10.1016\/j.eswa.2023.120496","article-title":"MFFCG\u2013Multi feature fusion for hyperspectral image classification using graph attention network","volume":"229","author":"Bhatti","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_16","first-page":"1","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, S., Sun, P., Song, Y., and Luo, P. (2023, January 1\u20136). Diffusiondet: Diffusion model for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01816"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Y., Chen, X., Lim, S.N., Torralba, A., Zhao, H., and Wang, S. (2023, January 1\u20136). Detecting everything in the open world: Towards universal object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Paris, France.","DOI":"10.1109\/CVPR52729.2023.01100"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jain, J., Li, J., Chiu, M.T., Hassani, A., Orlov, N., and Shi, H. (2023, January 17\u201324). Oneformer: One transformer to rule universal image segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00292"},{"key":"ref_23","unstructured":"Wu, J., Fu, R., Fang, H., Zhang, Y., Yang, Y., Xiong, H., Liu, H., and Xu, Y. (2024, January 3\u20135). Medsegdiff: Medical image segmentation with diffusion probabilistic model. Proceedings of the Medical Imaging with Deep Learning, PMLR, Paris, France."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jang, G., Lee, W., Son, S., and Lee, K.M. (2021, January 10\u201317). C2n: Practical generative noise modeling for real-world denoising. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00235"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Maleky, A., Kousha, S., Brown, M.S., and Brubaker, M.A. (2022, January 18\u201324). Noise2noiseflow: Realistic camera noise modeling without clean images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01711"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qin, H., Wang, X., and Li, H. (2021, January 10\u201317). Rethinking noise synthesis and modeling in raw denoising. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00455"},{"key":"ref_27","unstructured":"Chen, C., Chen, Q., Do, M.N., and Koltun, V. (November, January 27). Seeing motion in the dark. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Monakhova, K., Richter, S.R., Waller, L., and Koltun, V. (2022, January 18\u201324). Dancing under the stars: Video denoising in starlight. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01576"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Moseley, B., Bickel, V., L\u00f3pez-Francos, I.G., and Rana, L. (2021, January 20\u201325). Extreme low-light environment-driven image denoising over permanently shadowed lunar regions with a physical noise model. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00625"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, J., Yu, Y., Wu, S., Lei, C., and Xu, K. (2021, January 5\u20139). Rethinking noise modeling in extreme low-light environments. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428259"},{"key":"ref_31","first-page":"8520","article-title":"Physics-based noise modeling for extreme low-light photography","volume":"44","author":"Wei","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cao, Y., Liu, M., Liu, S., Wang, X., Lei, L., and Zuo, W. (2023, January 17\u201324). Physics-guided iso-dependent sensor noise modeling for extreme low-light photography. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00556"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chang, K.C., Wang, R., Lin, H.J., Liu, Y.L., Chen, C.P., Chang, Y.L., and Chen, H.T. (2020, January 23\u201328). Learning camera-aware noise models. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58586-0_21"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Abdelhamed, A., Lin, S., and Brown, M.S. (2018, January 18\u201323). A high-quality denoising dataset for smartphone cameras. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00182"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feng, H., Wang, L., Wang, Y., and Huang, H. (2022, January 10\u201314). Learnability enhancement for low-light raw denoising: Where paired real data meets noise modeling. Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, Portugal.","DOI":"10.1145\/3503161.3548186"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Xu, J., and Koltun, V. (2018, January 18\u201323). Learning to see in the dark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00347"},{"key":"ref_37","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Janesick, J., Klaasen, K., and Elliott, T. (1985, January 22\u201323). CCD charge collection efficiency and the photon transfer technique. Proceedings of the Solid-State Imaging Arrays, San Diego, CA, USA.","DOI":"10.1117\/12.950297"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1080\/01621459.1971.10482275","article-title":"Some properties of the range in samples from Tukey\u2019s symmetric lambda distributions","volume":"66","author":"Joiner","year":"1971","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/TED.2007.896718","article-title":"A comprehensive tool for modeling CMOS image-sensor-noise performance","volume":"54","author":"Gow","year":"2007","journal-title":"IEEE Trans. Electron Devices"},{"key":"ref_41","unstructured":"Abdelhamed, A., Brubaker, M.A., and Brown, M.S. (November, January 27). Noise flow: Noise modeling with conditional normalizing flows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"130719","DOI":"10.1109\/ACCESS.2020.3003351","article-title":"CA-GAN: Class-condition attention GAN for underwater image enhancement","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Anoosheh, A., Sattler, T., Timofte, R., Pollefeys, M., and Van Gool, L. (2019, January 20\u201324). Night-to-day image translation for retrieval-based localization. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794387"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Lakmal, H., and Dissanayake, M. (2023, January 24\u201325). Illuminating the Roads: Night-to-Day Image Translation for Improved Visibility at Night. Proceedings of the International Conference on Asia Pacific Advanced Network, Colombo, Sri Lanka.","DOI":"10.1007\/978-3-031-51135-6_2"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1109\/TPAMI.2022.3167175","article-title":"Learning enriched features for fast image restoration and enhancement","volume":"45","author":"Zamir","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","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_50","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/6\/646\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:47:05Z","timestamp":1760107625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/6\/646"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":50,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["sym16060646"],"URL":"https:\/\/doi.org\/10.3390\/sym16060646","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,23]]}}}