{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:06:53Z","timestamp":1760231213397,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T00:00:00Z","timestamp":1662249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"],"award-info":[{"award-number":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"]}]},{"name":"2019 Jiangsu Province Key Research and Development Plan by the Jiangsu Province Science and Technology","award":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"],"award-info":[{"award-number":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"]}]},{"name":"National Natural Science Foundation of China","award":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"],"award-info":[{"award-number":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"],"award-info":[{"award-number":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"]}]},{"name":"National Key Research and Development Program of China","award":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"],"award-info":[{"award-number":["KYCX22_1050","BE2019112","61901221","KYCX21_0872","2019YFD1100404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the global population surge, the consumption of nonrenewable resources and pollution emissions have reached an alarming level. Engineered bamboo is widely used in construction, mechanical and electrical product packaging, and other industries. Its main damage is the material fracture caused by the expansion of initial cracks. In order to accurately detect the length of crack propagation, digital image correlation technology can be used for calculation. At present, the traditional interpolation method is still used in the reconstruction of engineered bamboo speckle images for digital correlation technology, and the performance is relatively lagging. Therefore, this paper proposes a super-resolution reconstruction method of engineering-bamboo speckle images based on an attention-dense residual network. In this study, the residual network is improved by removing the BN layer, using the L1 loss function, introducing the attention model, and designing an attention-intensive residual block. An image super-resolution model based on the attention-dense residual network is proposed. Finally, the objective evaluation indexes PSNR and SSIM and subjective evaluation index MOS were used to evaluate the performance of the model. The ADRN method was 29.19 dB, 0.938, and 3.19 points in PSNR, SSIM, and MOS values. Compared to the traditional BICUBIC B-spline interpolation method, the speckle images reconstructed by this model increased by 8.55 dB, 0.323, and 1.43 points, respectively. Compared to the SRResNet method, the speckle images reconstructed by this model were increased by 4.53 dB, 0.111, and 0.14 points, respectively. The reconstructed speckle images of engineered bamboo were clearer, and the image features were more obvious, which could better identify the tip crack position of the engineered bamboo. The results show that the super-resolution reconstruction effect of engineered-bamboo speckle images can be effectively improved by adding the attention mechanism to the residual network. This method has great application value.<\/jats:p>","DOI":"10.3390\/s22176693","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Super-Resolution Reconstruction of Speckle Images of Engineered Bamboo Based on an Attention-Dense Residual Network"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3022-3913","authenticated-orcid":false,"given":"Wei","family":"Yu","sequence":"first","affiliation":[{"name":"Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3396-2842","authenticated-orcid":false,"given":"Zilong","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0680-6908","authenticated-orcid":false,"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5596-4219","authenticated-orcid":false,"given":"Yutu","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binli","family":"Gou","sequence":"additional","affiliation":[{"name":"Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.apm.2021.05.010","article-title":"Free vibration analysis of composite panels considering correlations of spatially distributed uncertain parameters","volume":"98","author":"Jiang","year":"2021","journal-title":"Appl. Math. Model."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.3390\/polym7081465","article-title":"Disposal Options of Bamboo Fabric-Reinforced Poly (Lactic) Acid Composites for Sustainable Packaging: Biodegradability and Recyclability","volume":"7","author":"Fazita","year":"2015","journal-title":"Polymers"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.conbuildmat.2018.06.139","article-title":"Strain method for synchronous dynamic measurement of elastic, shear modulus and Poisson\u2019s ratio of wood and wood composites","volume":"182","author":"Wang","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s11340-021-00694-w","article-title":"Optimal Aperture and Digital Speckle Optimization in Digital Image Correlation","volume":"61","author":"Wang","year":"2021","journal-title":"Exp. Mech."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s00371-021-02206-2","article-title":"Self-supervised monocular depth estimation based on image texture detail enhancement","volume":"37","author":"Li","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"014103","DOI":"10.1117\/1.OE.56.1.014103","article-title":"Automated initial guess in digital image correlation aided by Fourier\u2013Mellin transform","volume":"56","author":"Pan","year":"2017","journal-title":"Opt. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11265-020-01543-0","article-title":"Fractional Fourier-Radial Transform for Digital Image Recognition","volume":"93","year":"2021","journal-title":"J. Signal Process. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.optlaseng.2019.04.016","article-title":"Enhanced feature-based path-independent initial value estimation for robust point-wise digital image correlation","volume":"121","author":"Li","year":"2019","journal-title":"Opt. Laser Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106432","DOI":"10.1016\/j.optlaseng.2020.106432","article-title":"Full-automatic seed point selection and initialization for digital image correlation robust to large rotation and deformation","volume":"138","author":"Zou","year":"2021","journal-title":"Opt. Laser Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1080\/10589759.2016.1159306","article-title":"Experimental assessment of rectification algorithm in vision-based deflection measurement system","volume":"32","author":"Kohut","year":"2017","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Schleier, M., Adelmann, B., Esen, C., and Hellmann, R. (2022). Image Processing Algorithm for In Situ Monitoring Fiber Laser Remote Cutting by a High-Speed Camera. Sensors, 22.","DOI":"10.3390\/s22082863"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Yu, W. (2022). Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks. Sensors, 22.","DOI":"10.3390\/s22030745"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s40799-021-00476-y","article-title":"Two-Dimensional Meshfree-Based Digital Image Correlation for Strain-Displacement Measurement","volume":"46","author":"Drahman","year":"2022","journal-title":"Exp. Tech."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ooi, Y.K., and Ibrahim, H. (2021). Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review. Electronics, 10.","DOI":"10.3390\/electronics10070867"},{"key":"ref_15","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z.H. (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, New York, NY, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the European Conference on Computer Vision-ECCV, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/TPAMI.2020.3026899","article-title":"Parallax Attention for Unsupervised Stereo Correspondence Learning","volume":"44","author":"Wang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/TPAMI.2020.3021088","article-title":"Densely Residual Laplacian Super-Resolution","volume":"44","author":"Anwar","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.conbuildmat.2014.12.012","article-title":"Experimental study on stress\u2013strain relationships and failure mechanisms of parallel strand bamboo made from phyllostachys","volume":"77","author":"Huang","year":"2015","journal-title":"Constr. Build. Mater."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10704-021-00581-4","article-title":"Mode-II interlaminar fracture of composite materials in the presence of randomly distributed defects","volume":"231","author":"Kumar","year":"2021","journal-title":"Int. J. Fract."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103440","DOI":"10.1109\/ACCESS.2021.3096548","article-title":"RBDN: Residual Bottleneck Dense Network for Image Super-Resolution","volume":"9","author":"An","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1049\/ipr2.12364","article-title":"Deep coordinate attention network for single image super-resolution","volume":"16","author":"Xie","year":"2022","journal-title":"IET Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Long, J.S., Ma, G.Z., Song, E.M., and Jin, R.C. (2021). Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation. Sensors, 21.","DOI":"10.3390\/s21093232"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105594","DOI":"10.1016\/j.applanim.2022.105594","article-title":"Automatically recognizing four-legged animal behaviors to enhance welfare using spatial temporal graph convolutional networks","volume":"249","author":"Zhao","year":"2022","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_27","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":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, H., Xie, C., Fei, Y., and Tao, H. (2021). Attention Mechanisms in CNN-Based Single Image Super-Resolution: A Brief Review and a New Perspective. Electronics, 10.","DOI":"10.3390\/electronics10101187"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1080\/10589759.2011.645820","article-title":"Comparative analysis of NDE techniques with image processing","volume":"27","author":"Rathod","year":"2012","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s13007-022-00929-4","article-title":"Deep learning for detecting herbicide weed control spectrum in turfgrass","volume":"18","author":"Jin","year":"2022","journal-title":"Plant Methods"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6693\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:23:11Z","timestamp":1760142191000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,4]]},"references-count":30,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176693"],"URL":"https:\/\/doi.org\/10.3390\/s22176693","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,9,4]]}}}