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While low-magnification microscopy offers a wide field of view (FOV) but insufficient resolution for meaningful assessment, high-magnification microscopy offers more detailed but narrower FOV images. To obtain a high-magnification, large FOV image, current practice relies on stitching multiple high-magnification images, which is labor-intensive and inefficient. To address this, we propose a multi-reference-based super-resolution (RefSR) framework specifically designed for blood cell microscopy. By using paired low- and high-magnification images directly in the dataset construction process, we ensure robustness in real-world microscopy. Building on this, we develop a region-aware architecture that selectively applies RefSR to WBC regions and single-image SR to background regions, thereby maintaining performance while reducing computational cost. Furthermore, we introduce a Cross-Reference Attention Module that explicitly models interactions between multiple high-magnification references, which conventional multi-RefSR methods ignore. Experiments show that our framework creates large FOV images with sharp WBC morphology and fewer artifacts than existing SR approaches, while achieving higher efficiency than the stitching method. These results demonstrate that our method provides a practical solution for accurate and scalable blood cell image analysis, effectively eliminating the need for conventional stitching.<\/jats:p>","DOI":"10.1093\/jcde\/qwag041","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T12:07:14Z","timestamp":1777637234000},"page":"166-182","source":"Crossref","is-referenced-by-count":0,"title":["Cross-reference attention for region-aware super-resolution of blood cell images"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9222-7451","authenticated-orcid":false,"given":"Gyuwon","family":"Kang","sequence":"first","affiliation":[{"name":"Center for Bionics, Korea Institute of Science and Technology , Seoul 02792 ,","place":["Republic of Korea"]},{"name":"Department of Mechanical Engineering, Korea University , Seoul 02841 ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungmin","family":"Hwang","sequence":"additional","affiliation":[{"name":"Doping Control Center, Korea Institute of Science and Technology , Seoul 02792 ,","place":["Republic of Korea"]},{"name":"Department of Biotechnology, Yonsei University College of Life Science and Biotechnology , Seoul 03722 ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoeseph","family":"Cho","sequence":"additional","affiliation":[{"name":"Doping Control Center, Korea Institute of Science and Technology , Seoul 02792 ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junghyun","family":"Son","sequence":"additional","affiliation":[{"name":"Doping Control Center, Korea Institute of Science and Technology , Seoul 02792 ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7340-897X","authenticated-orcid":false,"given":"Deukhee","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Bionics, Korea Institute of Science and Technology , Seoul 02792 ,","place":["Republic of Korea"]},{"name":"Division of AI Robotics, KIST School, University of Science and Technology , Seoul 02792 ,","place":["Republic of Korea"]},{"name":"Yonsei-KIST Convergence Research Institute, Yonsei University , Seoul 03722 ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-7932","authenticated-orcid":false,"given":"Gunwoo","family":"Noh","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea University , Seoul 02841 ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"2026072010152648600_bib1","doi-asserted-by":"publisher","first-page":"105474","DOI":"10.1016\/j.dib.2020.105474","article-title":"A dataset of microscopic peripheral blood cell images for development of automatic recognition systems","volume":"30","author":"Acevedo","year":"2020","journal-title":"Data in Brief"},{"key":"2026072010152648600_bib2","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.3390\/diagnostics11101881","article-title":"The value of a complete blood count (cbc) for sepsis diagnosis and prognosis","volume":"11","author":"Agnello","year":"2021","journal-title":"Diagnostics"},{"key":"2026072010152648600_bib3","first-page":"261","article-title":"White blood cells detection using saliency maps and sample learning for leukemia severity analysis","volume-title":"International Conference on Data, Electronics and Computing","author":"Banerjee","year":"2023"},{"key":"2026072010152648600_bib4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1145\/1531326.1531330","article-title":"Patchmatch: A randomized correspondence algorithm for structural image editing","volume":"28","author":"Barnes","year":"2009","journal-title":"ACM Trans. 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