{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:42:34Z","timestamp":1781764954696,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,2]],"date-time":"2016-11-02T00:00:00Z","timestamp":1478044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61501156"],"award-info":[{"award-number":["61501156"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NTU-ifood","award":["2014"],"award-info":[{"award-number":["2014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4\u00d7, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.<\/jats:p>","DOI":"10.3390\/s16111836","type":"journal-article","created":{"date-parts":[[2016,11,2]],"date-time":"2016-11-02T10:00:35Z","timestamp":1478080835000},"page":"1836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2364-0479","authenticated-orcid":false,"given":"Xiwei","family":"Huang","sequence":"first","affiliation":[{"name":"Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"School of Microelectronics, Southeast University, Wuxi 214135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Han","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Southeast University, Wuxi 214135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailong","family":"Rong","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Southeast University, Wuxi 214135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiping","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Southeast University, Wuxi 214135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Southeast University, Wuxi 214135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.mee.2014.09.024","article-title":"Point-of-care testing (POCT) diagnostic systems using microfluidic lab-on-a-chip technologies","volume":"132","author":"Jung","year":"2015","journal-title":"Microelectron. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1039\/c0lc00587h","article-title":"Integrated systems for rapid point of care (POC) blood cell analysis","volume":"11","author":"Gwyer","year":"2011","journal-title":"Lab Chip"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1146\/annurev-bioeng-092515-010849","article-title":"Lensless imaging and sensing","volume":"18","author":"Ozcan","year":"2016","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, X., Guo, J., Yan, M., Kang, Y., and Yu, H. (2014). A contact-imaging based microfluidic cytometer with machine-learning for single-frame super-resolution processing. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0104539"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.vlsi.2014.07.004","article-title":"A robust recognition error recovery for micro-flow cytometer by machine-learning enhanced single-frame super-resolution processing","volume":"51","author":"Huang","year":"2015","journal-title":"Integration"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1039\/B713695A","article-title":"Ultra wide-field lens-free monitoring of cells on-chip","volume":"8","author":"Ozcan","year":"2008","journal-title":"Lab Chip"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1039\/c0lc00213e","article-title":"Sub-pixel resolving optofluidic microscope for on-chip cell imaging","volume":"10","author":"Zheng","year":"2010","journal-title":"Lab Chip"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1016\/j.bios.2010.07.081","article-title":"High-content analysis of single cells directly assembled on CMOS sensor based on color imaging","volume":"26","author":"Tanaka","year":"2010","journal-title":"Biosens. Bioelectron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.bios.2012.05.022","article-title":"Lens-free shadow image based high-throughput continuous cell monitoring technique","volume":"38","author":"Jin","year":"2012","journal-title":"Biosens. Bioelectron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1109\/TCSI.2007.902409","article-title":"Contact imaging: Simulation and experiment","volume":"54","author":"Ji","year":"2007","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/TBME.2015.2419233","article-title":"A dual-mode large-arrayed CMOS ISFET sensor for accurate and high-throughput pH sensing in biomedical diagnosis","volume":"62","author":"Huang","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","article-title":"Super-resolution image reconstruction: A technical overview","volume":"20","author":"Park","year":"2003","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1039\/c0lc00684j","article-title":"Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array","volume":"11","author":"Bishara","year":"2011","journal-title":"Lab Chip"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e346","DOI":"10.1038\/lsa.2015.119","article-title":"Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution","volume":"4","author":"Sobieranski","year":"2015","journal-title":"Light Sci. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MDAT.2015.2424418","article-title":"A single-frame superresolution algorithm for lab-on-a-chip lensless microfluidic imaging","volume":"32","author":"Huang","year":"2015","journal-title":"IEEE Des. Test."},{"key":"ref_16","unstructured":"Wang, T., Huang, X., Jia, Q., Yan, M., Yu, H., and Yeo, K.-S. (2012, January 28\u201330). A super-resolution CMOS image sensor for bio-microfluidic imaging. Proceedings of the Biomedical Circuits and Systems, Hsinchu, Taiwan."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1039\/C4LC01358A","article-title":"Rapid imaging, detection and quantification of giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning","volume":"15","author":"Koydemir","year":"2015","journal-title":"Lab Chip"},{"key":"ref_18","first-page":"5529","article-title":"Machine learning in cell biology\u2014Teaching computers to recognize phenotypes","volume":"126","author":"Sommer","year":"2013","journal-title":"J. Cell Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1023\/A:1026501619075","article-title":"Learning low-level vision","volume":"40","author":"Freeman","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/38.988747","article-title":"Example-based super-resolution","volume":"22","author":"Freeman","year":"2002","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1944846.1944852","article-title":"Image and video upscaling from local self-examples","volume":"30","author":"Freedman","year":"2011","journal-title":"ACM Trans. Graph."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, J., Lin, Z., and Cohen, S.D. (2013, January 23\u201328). Fast Image Super-Resolution Based on in-Place Example Regression. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.141"},{"key":"ref_23","unstructured":"Sun, J., Zheng, N., Tao, H., and Shum, H. (2003, January 16\u201322). Image Hallucination with Primal Sketch Priors. Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, WI, USA."},{"key":"ref_24","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_25","unstructured":"Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., and Ashok, A. (July, January 26). ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","first-page":"115032","DOI":"10.1088\/0960-1317\/20\/11\/115032","article-title":"Bonding strength of pressurized microchannels fabricated by polydimethylsiloxane and silicon","volume":"20","author":"Wu","year":"2010","journal-title":"J. Micromech. Microeng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/11\/1836\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:34:33Z","timestamp":1760211273000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/11\/1836"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,2]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2016,11]]}},"alternative-id":["s16111836"],"URL":"https:\/\/doi.org\/10.3390\/s16111836","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,11,2]]}}}