{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:46:55Z","timestamp":1760240815085,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"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>Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback\u2013Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.<\/jats:p>","DOI":"10.3390\/s19194076","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T10:48:14Z","timestamp":1568976494000},"page":"4076","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology"],"prefix":"10.3390","volume":"19","author":[{"given":"Yifan","family":"Yang","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Yang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bei","family":"Li","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1016\/S1872-2040(11)60577-X","article-title":"In vivo Raman Imaging of Mice Ear","volume":"40","author":"Xue","year":"2012","journal-title":"Chin. J. Anal. Chem."},{"key":"ref_2","first-page":"4082","article-title":"Research of the Raman Signal De-Noising Method Based on Feature Extraction","volume":"36","author":"Fan","year":"2016","journal-title":"Guang Pu"},{"key":"ref_3","first-page":"369","article-title":"Detection of Chemical Additives in Food Using Raman Chemical Imaging System","volume":"38","author":"Chen","year":"2017","journal-title":"Chem. J. Chin. Univ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.jpba.2014.12.039","article-title":"Quantification of low drug concentration in model formulations with multivariate analysis using surface enhanced Raman chemical imaging","volume":"107","author":"Firkala","year":"2015","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.aca.2015.08.025","article-title":"Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells","volume":"894","author":"Bruckner","year":"2015","journal-title":"Anal. Chim. Acta"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.conbuildmat.2016.03.153","article-title":"Chemical imaging of historical mortars by Raman microscopy","volume":"114","author":"Schmid","year":"2016","journal-title":"Constr. Build. Mater."},{"key":"ref_7","first-page":"1257","article-title":"Study on Apoptosis Process of CaSki via Fast Line. scanning Raman Imaging","volume":"37","author":"Zhang","year":"2016","journal-title":"Chem. J. Chin. Univ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1038\/s41377-018-0082-1","article-title":"Light Science Applications. Near-resonance enhanced label-free stimulated Raman scattering microscopy with spatial resolution near 130 nm","volume":"7","author":"Bi","year":"2018","journal-title":"Light Sci. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e20","DOI":"10.1038\/lsa.2012.20","article-title":"Light Science Applications. Metallic subwavelength-cavity semiconductor nanolasers","volume":"1","author":"Ding","year":"2012","journal-title":"Light Sci. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41377-018-0003-3","article-title":"High-resolution multimodal flexible coherent Raman endoscope","volume":"7","author":"Lombardini","year":"2018","journal-title":"Light Sci. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e17098","DOI":"10.1038\/lsa.2017.98","article-title":"Subnanometer-resolved chemical imaging via multivariate analysis of tip-enhanced Raman maps","volume":"6","author":"Song","year":"2017","journal-title":"Light Sci. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5448","DOI":"10.1039\/C6AY01130F","article-title":"Determination of Sudan I in duck feed by microscopic image processing and confocal Raman spectroscopy","volume":"8","author":"Li","year":"2016","journal-title":"Anal. Methods"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chao, K., Dhakal, S., Qin, J., Kim, M., and Peng, Y. (2018). A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation. Appl. Sci., 8.","DOI":"10.3390\/app8030431"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.tifs.2017.01.012","article-title":"Raman imaging for food quality and safety evaluation: Fundamentals and applications","volume":"62","author":"Yaseen","year":"2017","journal-title":"Trends Food Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"85290","DOI":"10.18632\/oncotarget.19668","article-title":"Novel strategies of Raman imaging for brain tumor research","volume":"8","author":"Anna","year":"2017","journal-title":"Oncotarget"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lohumi, S., Lee, H., Kim, M., Qin, J., and Cho, B.K. (2018). Raman Imaging for the Detection of Adulterants in Paprika Power: A Comparison of Data Analysis Methods. Appl. Sci., 8.","DOI":"10.3390\/app8040485"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hauke, K., Kehren, J., B\u00f6hme, N., Zimmer, S., and Geisler, T. (2019). In Situ Hyperspectral Raman Imaging: A New Method to Investigate Sintering Processes of Ceramic Material at High-temperature. Appl. Sci., 9.","DOI":"10.3390\/app9071310"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.saa.2018.01.043","article-title":"Raman imaging of carrier distribution in the channel of an ionic liquid-gated transistor fabricated with regioregular poly(3-hexylthiophene)","volume":"197","author":"Wada","year":"2018","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.saa.2018.02.058","article-title":"Angiogenesis\u2014A crucial step in breast cancer growth, progression and dissemination by Raman imaging","volume":"198","author":"Abramczyk","year":"2018","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/1732196","article-title":"Chemical Analysis of Air Particulate Matter Trapped by a Porous Material, Synthesized from Silica Fume and Sodium Alginate","volume":"2019","author":"Bilo","year":"2019","journal-title":"J. Nanomater."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"aaa8870","DOI":"10.1126\/science.aaa8870","article-title":"Vibrational spectroscopic imaging of living systems: An emerging platform for biology and medicine","volume":"350","author":"Cheng","year":"2015","journal-title":"Science"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1002\/jrs.2525","article-title":"Multivariate data analysis for Raman spectroscopic imaging","volume":"40","author":"Shinzawa","year":"2010","journal-title":"J. Raman Spectrosc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: A fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.cbpa.2016.04.005","article-title":"High-speed Raman imaging of cellular processes","volume":"33","author":"Ando","year":"2016","journal-title":"Curr. Opin. Chem. Biol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.jfoodeng.2016.11.016","article-title":"Line-scan Raman imaging and spectroscopy platform for surface and subsurface evaluation of food safety and quality","volume":"198","author":"Qin","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1364\/JOSA.61.000001","article-title":"Lightness and retinex theory","volume":"61","author":"Land","year":"1971","journal-title":"J. Opt. Soc. Am."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/83.557356","article-title":"Properties and performance of a center\/surround retinex","volume":"6","author":"Jobson","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1145\/1360612.1360666","article-title":"Edge-preserving decompositions for multi-scale tone and detail manipulation","volume":"27","author":"Farbman","year":"2008","journal-title":"ACM Trans. Graph."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, Q., Tan, K.H., and Ahuja, N. (2009, January 20\u201325). In Real-time O(1) bilateral filtering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206542"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Jian, S., and Tang, X. (2010). Guided Image Filtering, European Conference on Computer Vision 2010, Springer.","DOI":"10.1007\/978-3-642-15549-9_1"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1145\/2010324.1964964","article-title":"Domain transform for edge-aware image and video processing","volume":"30","author":"Gastal","year":"2011","journal-title":"ACM Trans. Graph."},{"key":"ref_33","first-page":"3406","article-title":"Research on the Method of Eliminating Noise and Background in the Meantime in Detecting Ethanol Contention Based on Raman Spectra","volume":"35","author":"Han","year":"2015","journal-title":"Guang pu"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image Super-Resolution via Sparse Representation","volume":"19","author":"Yang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zeyde, R., Elad, M., and Protter, M. (2012). On Single Image Scale-Up Using Sparse-Representations. International Conference on Curves & Surfaces, Springer.","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Timofte, R., De, V., and Gool, L.V. (2013, January 1\u20138). Anchored Neighborhood Regression for Fast Example-Based Super-Resolution. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.241"},{"key":"ref_37","unstructured":"Chang, H., Yeung, D.Y., and Xiong, Y. (July, January 27). Super-resolution through neighbor embedding. Proceedings of the IEEE Computer Society Conference on Computer Vision & Pattern Recognition, Washington, DC, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.neucom.2016.05.008","article-title":"Single image super resolution based on multiscale local similarity and neighbor embedding","volume":"207","author":"Pan","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_39","unstructured":"Fiset, P.O., Soussi-Gounni, A., Christodoulopoulos, P., Tulic, M., Sobol, S.E., Frenkiel, S., Lavigne, F., Lamkhioued, B., and Hamid, Q. (2005, January 14). A measure for evaluation of the information content in color images. Proceedings of the IEEE International Conference on Image Processing, Genova, Italy."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4076\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:22:34Z","timestamp":1760188954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4076"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,20]]},"references-count":39,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194076"],"URL":"https:\/\/doi.org\/10.3390\/s19194076","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,9,20]]}}}