{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:21:47Z","timestamp":1783696907866,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and Development Program of the Ministry of Trade, Industry, and Energy (MOTIE)","award":["RS-2022-00154973"],"award-info":[{"award-number":["RS-2022-00154973"]}]},{"name":"Research and Development Program of the Ministry of Trade, Industry, and Energy (MOTIE)","award":["P0017011"],"award-info":[{"award-number":["P0017011"]}]},{"name":"Research and Development Program of the Ministry of Trade, Industry, and Energy (MOTIE)","award":["P0012451"],"award-info":[{"award-number":["P0012451"]}]},{"DOI":"10.13039\/501100003662","name":"Korea Evaluation Institute of Industrial Technology (KEIT)","doi-asserted-by":"publisher","award":["RS-2022-00154973"],"award-info":[{"award-number":["RS-2022-00154973"]}],"id":[{"id":"10.13039\/501100003662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003662","name":"Korea Evaluation Institute of Industrial Technology (KEIT)","doi-asserted-by":"publisher","award":["P0017011"],"award-info":[{"award-number":["P0017011"]}],"id":[{"id":"10.13039\/501100003662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003662","name":"Korea Evaluation Institute of Industrial Technology (KEIT)","doi-asserted-by":"publisher","award":["P0012451"],"award-info":[{"award-number":["P0012451"]}],"id":[{"id":"10.13039\/501100003662","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea Institute for Advancement of Technology (KIAT)","award":["RS-2022-00154973"],"award-info":[{"award-number":["RS-2022-00154973"]}]},{"name":"Korea Institute for Advancement of Technology (KIAT)","award":["P0017011"],"award-info":[{"award-number":["P0017011"]}]},{"name":"Korea Institute for Advancement of Technology (KIAT)","award":["P0012451"],"award-info":[{"award-number":["P0012451"]}]},{"name":"IC Design Education Center (IDEC)","award":["RS-2022-00154973"],"award-info":[{"award-number":["RS-2022-00154973"]}]},{"name":"IC Design Education Center (IDEC)","award":["P0017011"],"award-info":[{"award-number":["P0017011"]}]},{"name":"IC Design Education Center (IDEC)","award":["P0012451"],"award-info":[{"award-number":["P0012451"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances.<\/jats:p>","DOI":"10.3390\/s24113608","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T08:19:42Z","timestamp":1717402782000},"page":"3608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7948-1559","authenticated-orcid":false,"given":"Minh-Thien","family":"Duong","sequence":"first","affiliation":[{"name":"Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bao-Tran","family":"Nguyen Thi","sequence":"additional","affiliation":[{"name":"Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3407-6236","authenticated-orcid":false,"given":"Seongsoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Intelligent Semiconductor, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3979-8315","authenticated-orcid":false,"given":"Min-Cheol","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hartbauer, M. (2023). A Simple Denoising Algorithm for Real-World Noisy Camera Images. J. Imaging, 9.","DOI":"10.3390\/jimaging9090185"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"132147","DOI":"10.1109\/ACCESS.2023.3336411","article-title":"DMT-Net: Deep Multiple Networks for Low-Light Image Enhancement Based on Retinex Model","volume":"11","author":"Duong","year":"2023","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1038\/s43588-023-00568-2","article-title":"Spatial redundancy transformer for self-supervised fluorescence image denoising","volume":"3","author":"Li","year":"2023","journal-title":"Nat. Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Oliveira-Saraiva, D., Mendes, J., Leote, J., Gonzalez, F.A., Garcia, N., Ferreira, H.A., and Matela, N. (2023). Make It Less Complex: Autoencoder for Speckle Noise Removal\u2014Application to Breast and Lung Ultrasound. J. Imaging, 9.","DOI":"10.3390\/jimaging9100217"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Krichen, M. (2023). Convolutional neural networks: A survey. Computers, 12.","DOI":"10.3390\/computers12080151"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neunet.2020.07.025","article-title":"Deep learning on image denoising: An overview","volume":"131","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.1007\/s10462-022-10305-2","article-title":"Image denoising in the deep learning era","volume":"56","author":"Izadi","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.image.2018.06.016","article-title":"Mixed Gaussian-impulse noise reduction from images using convolutional neural network","volume":"68","author":"Islam","year":"2018","journal-title":"Signal Process. Image Commun."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Solovyeva, E., and Abdullah, A. (2022). Dual autoencoder network with separable convolutional layers for denoising and deblurring images. J. Imaging, 8.","DOI":"10.3390\/jimaging8090250"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102258","DOI":"10.1016\/j.displa.2022.102258","article-title":"Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images","volume":"74","author":"Paul","year":"2022","journal-title":"Displays"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"6360","DOI":"10.1109\/TPAMI.2021.3088914","article-title":"Plug-and-play image restoration with deep denoiser prior","volume":"44","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","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 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.486"},{"key":"ref_14","unstructured":"Liu, D., Wen, B., Fan, Y., Loy, C.C., and Huang, T.S. (2018). Non-local recurrent network for image restoration. Adv. Neural Inf. Process. Syst., 31."},{"key":"ref_15","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., and Fu, Y. (2019). Residual non-local attention networks for image restoration. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., and Zhang, L. (2017, January 21\u201326). Learning deep CNN denoiser prior for image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.300"},{"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":"Oh, J., and Hong, M.C. (2022). Low-light image enhancement using hybrid deep-learning and mixed-norm loss functions. Sensors, 22.","DOI":"10.3390\/s22186904"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/83.862633","article-title":"Adaptive wavelet thresholding for image denoising and compression","volume":"9","author":"Chang","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4334","DOI":"10.1109\/TGRS.2018.2815281","article-title":"Multispectral satellite image denoising via adaptive cuckoo search-based Wiener filter","volume":"56","author":"Suresh","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s13640-018-0264-z","article-title":"Improved BM3D image denoising using SSIM-optimized Wiener filter","volume":"2018","author":"Hasan","year":"2018","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ishikawa, A., Tajima, H., and Fukushima, N. (2020, January 5\u20137). Halide implementation of weighted median filter. Proceedings of the International Workshop on Advanced Imaging Technology (IWAIT), Yogyakarta, Indonesia.","DOI":"10.1117\/12.2566536"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5475","DOI":"10.1109\/TIP.2018.2857448","article-title":"A robust edge detection approach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering","volume":"27","author":"Mafi","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Iqbal, N., Ali, S., Khan, I., and Lee, B.M. (2019). Adaptive edge preserving weighted mean filter for removing random-valued impulse noise. Symmetry, 11.","DOI":"10.3390\/sym11030395"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"163677","DOI":"10.1016\/j.ijleo.2019.163677","article-title":"Adaptive total variation L1 regularization for salt and pepper image denoising","volume":"208","author":"Thanh","year":"2020","journal-title":"Optik"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TGRS.2019.2947333","article-title":"Hyperspectral image denoising with total variation regularization and nonlocal low-rank tensor decomposition","volume":"58","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"31742","DOI":"10.1109\/ACCESS.2021.3061062","article-title":"A residual dense u-net neural network for image denoising","volume":"9","author":"Dalmau","year":"2021","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ali, A.M., Benjdira, B., Koubaa, A., El-Shafai, W., Khan, Z., and Boulila, W. (2023). Vision transformers in image restoration: A survey. Sensors, 23.","DOI":"10.3390\/s23052385"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jamil, S., Jalil Piran, M., and Kwon, O.J. (2023). A comprehensive survey of transformers for computer vision. Drones, 7.","DOI":"10.2139\/ssrn.4332114"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fan, C.M., Liu, T.J., and Liu, K.H. (June, January 28). SUNet: Swin transformer UNet for image denoising. Proceedings of the 2022 IEEE International Symposium on Circuits and Systems (ISCAS), Austin, TX, USA.","DOI":"10.1109\/ISCAS48785.2022.9937486"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., 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_33","doi-asserted-by":"crossref","unstructured":"Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., and Van Gool, L. (2023, January 2\u20136). Diffir: Efficient diffusion model for image restoration. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01204"},{"key":"ref_34","unstructured":"Yang, C., Liang, L., and Su, Z. (2023). Real-World Denoising via Diffusion Model. arXiv."},{"key":"ref_35","unstructured":"Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., and Xia, S.T. (2024). MambaIR: A Simple Baseline for Image Restoration with State-Space Model. arXiv."},{"key":"ref_36","unstructured":"Paul, A., Kundu, A., Chaki, N., Dutta, D., and Jha, C. (2022). Multimedia Tools and Applications, Springer."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e3","DOI":"10.23915\/distill.00003","article-title":"Deconvolution and checkerboard artifacts","volume":"1","author":"Odena","year":"2016","journal-title":"Distill"},{"key":"ref_38","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, M., Zhang, W., Chen, X., Liu, Y., and Niu, J. (2022). An End-to-End Atrous Spatial Pyramid Pooling and Skip-Connections Generative Adversarial Segmentation Network for Building Extraction from High-Resolution Aerial Images. Appl. Sci., 12.","DOI":"10.3390\/app12105151"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N., and Petersson, L. (2021). Attention-based real image restoration. IEEE Trans. Neural Netw. Learn. Syst., 1\u201313. early access.","DOI":"10.1109\/TNNLS.2021.3131739"},{"key":"ref_42","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":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","unstructured":"Park, J., Woo, S., Lee, J.Y., and Kweon, I.S. (2018). Bam: Bottleneck attention module. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shafiq, M., and Gu, Z. (2022). Deep residual learning for image recognition: A survey. Appl. Sci., 12.","DOI":"10.3390\/app12188972"},{"key":"ref_45","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/ICIP.1994.413553","article-title":"Two deterministic half-quadratic regularization algorithms for computed imaging","volume":"Volume 2","author":"Charbonnier","year":"1994","journal-title":"Proceedings of the IEEE International Conference on Image Processing"},{"key":"ref_47","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\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_48","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_49","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). Ntire 2017 challenge on single image super-resolution: Dataset and study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Abdelhamed, A., Lin, S., and Brown, M.S. (2018, January 18\u201322). A high-quality denoising dataset for smartphone cameras. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00182"},{"key":"ref_52","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_53","first-page":"416","article-title":"A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics","volume":"Volume 2","author":"Martin","year":"2001","journal-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV)"},{"key":"ref_54","unstructured":"Franzen, R. (2022, June 22). Kodak Lossless True Color Image Suite. 1999. Volume 4, p. 9. Available online: http:\/\/r0k.us\/graphics\/kodak."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5201\/ipol.2015.125","article-title":"The noise clinic: A blind image denoising algorithm","volume":"5","author":"Lebrun","year":"2015","journal-title":"Image Process. Line"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3608\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:53:02Z","timestamp":1760107982000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":55,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113608"],"URL":"https:\/\/doi.org\/10.3390\/s24113608","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,3]]}}}