{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:23:16Z","timestamp":1773717796544,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T00:00:00Z","timestamp":1678492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) grant funded by the Korean government","doi-asserted-by":"publisher","award":["2022R1A2C200289711"],"award-info":[{"award-number":["2022R1A2C200289711"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the demand for thermal information increases in industrial fields, numerous studies have focused on enhancing the quality of infrared images. Previous studies have attempted to independently overcome one of the two main degradations of infrared images, fixed pattern noise (FPN) and blurring artifacts, neglecting the other problems, to reduce the complexity of the problems. However, this is infeasible for real-world infrared images, where two degradations coexist and influence each other. Herein, we propose an infrared image deconvolution algorithm that jointly considers FPN and blurring artifacts in a single framework. First, an infrared linear degradation model that incorporates a series of degradations of the thermal information acquisition system is derived. Subsequently, based on the investigation of the visual characteristics of the column FPN, a strategy to precisely estimate FPN components is developed, even in the presence of random noise. Finally, a non-blind image deconvolution scheme is proposed by analyzing the distinctive gradient statistics of infrared images compared with those of visible-band images. The superiority of the proposed algorithm is experimentally verified by removing both artifacts. Based on the results, the derived infrared image deconvolution framework successfully reflects a real infrared imaging system.<\/jats:p>","DOI":"10.3390\/s23063033","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:28:33Z","timestamp":1678678113000},"page":"3033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Infrared Image Deconvolution Considering Fixed Pattern Noise"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-8731","authenticated-orcid":false,"given":"Haegeun","family":"Lee","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5771-929X","authenticated-orcid":false,"given":"Moon Gi","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kaplan, H. (2007). Practical Applications of Infrared Thermal Sensing and Imaging Equipment, SPIE Press.","DOI":"10.1117\/3.725072"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00138-013-0570-5","article-title":"Thermal cameras and applications: A survey","volume":"25","author":"Gade","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1088\/0143-0807\/32\/5\/B01","article-title":"Infrared thermal imaging: Fundamentals, research and applications","volume":"32","author":"Planinsic","year":"2011","journal-title":"Eur. J. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13040","DOI":"10.1038\/s41598-017-13595-7","article-title":"Infrared light field imaging system free of fixed-pattern noise","volume":"7","author":"Coelho","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kee, E., Paris, S., Chen, S., and Wang, J. (2011, January 8\u201310). Modeling and removing spatially-varying optical blur. Proceedings of the 2011 IEEE International Conference on Computational Photography (ICCP), Pittsburgh, PA, USA.","DOI":"10.1109\/ICCPHOT.2011.5753120"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1145\/1276377.1276464","article-title":"Image and Depth from a Conventional Camera with a Coded Aperture","volume":"26","author":"Levin","year":"2007","journal-title":"ACM Trans. Graph."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3556","DOI":"10.1109\/TIP.2018.2825112","article-title":"Permuted Coordinate-Wise Optimizations Applied to Lp-Regularized Image Deconvolution","volume":"27","author":"Han","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.apm.2018.12.021","article-title":"Non-convex and non-smooth variational decomposition for image restoration","volume":"69","author":"Liming","year":"2019","journal-title":"Appl. Math. Model."},{"key":"ref_9","unstructured":"Krishnan, D., and Fergus, R. (2009). Fast image deconvolution using hyper-Laplacian priors. Adv. Neural Inf. Process. Syst., 22."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108307","DOI":"10.1016\/j.sigpro.2021.108307","article-title":"Automatic prior selection for image deconvolution: Statistical modeling on natural images","volume":"189","author":"Lee","year":"2021","journal-title":"Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.neucom.2020.08.053","article-title":"Image restoration using overlapping group sparsity on hyper-laplacian prior of image gradient","volume":"420","author":"Jon","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_12","first-page":"223","article-title":"Thermal image enhancement through the deconvolution methods for low-cost infrared cameras","volume":"15","author":"Lai","year":"2018","journal-title":"Quant. Infrared Thermogr. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Han, J., Lee, H., and Kang, M.G. (2021). Thermal image restoration based on LWIR sensor statistics. Sensors, 21.","DOI":"10.3390\/s21165443"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.phpro.2011.11.058","article-title":"Calibration-based NUC method in real-time based on IRFPA","volume":"22","author":"Sheng","year":"2011","journal-title":"Phys. Procedia"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/83.777098","article-title":"Nonuniformity correction of infrared image sequences using the constant-statistics constraint","volume":"8","author":"Harris","year":"1999","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1364\/JOSAA.19.001737","article-title":"An algebraic algorithm for nonuniformity correction in focal-plane arrays","volume":"19","author":"Ratliff","year":"2002","journal-title":"JOSA A"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1364\/OL.36.000172","article-title":"Total variation approach for adaptive nonuniformity correction in focal-plane arrays","volume":"36","author":"Vera","year":"2011","journal-title":"Opt. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1117\/1.601890","article-title":"Nonuniformity two-point linear correction errors in infrared focal plane arrays","volume":"37","author":"Friedenberg","year":"1998","journal-title":"Opt. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Miao, L.I., Xu, Q., Zhang, M.T., Sun, D.X., and Liu, Y.N. (2009, January 17\u201319). Real-time implementation of multi-point nonuniformity correction for IRFPA based on FPGA. Proceedings of the International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, Beijing, China.","DOI":"10.1117\/12.835055"},{"key":"ref_20","first-page":"104","article-title":"Polynomial fitting based on nonuniformity correction of infrared focal plane arrays","volume":"35","author":"Li","year":"2005","journal-title":"Laser Infrared"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/TC.1973.5009169","article-title":"The application of constrained least squares estimation to image restoration by digital computer","volume":"100","author":"Hunt","year":"1973","journal-title":"IEEE Trans. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. Nonlinear Phenom."},{"key":"ref_23","unstructured":"Farsiu, S., Robinson, D., Elad, M., and Milanfar, P. (2003, January 14\u201317). Fast and robust super-resolution. Proceedings of the 2003 International Conference on Image Processing (Cat. No. 03CH37429), Barcelona, Spain."},{"key":"ref_24","unstructured":"Tappen, M.F., Russell, B.C., and Freeman, W.T. (2003, January 12). Exploiting the sparse derivative prior for super-resolution and image demosaicing. Proceedings of the Third International Workshop Statistical and Computational Theories of Vision, Nice, France."},{"key":"ref_25","first-page":"683","article-title":"Image restoration by matching gradient distributions","volume":"34","author":"Cho","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zoran, D., and Weiss, Y. (2011, January 6\u201313). From learning models of natural image patches to whole image restoration. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126278"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s11263-016-0901-x","article-title":"Multi-modal rgb\u2013depth\u2013thermal human body segmentation","volume":"118","author":"Palmero","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Treible, W., Saponaro, P., Sorensen, S., Kolagunda, A., O\u2019Neal, M., Phelan, B., Sherbondy, K., and Kambhamettu, C. (2017, January 21\u201326). Cats: A color and thermal stereo benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.22"},{"key":"ref_29","unstructured":"(2023, January 02). Flir Thermal Dataset for Algorithm Training. FLIR ADAS. Available online: https:\/\/www.flir.in\/oem\/adas\/adas-dataset-form\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Levin, A., Weiss, Y., Durand, F., and Freeman, W.T. (2009, January 20\u201325). Understanding and evaluating blind deconvolution algorithms. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206815"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1364\/AO.38.000772","article-title":"Statistical algorithm for nonuniformity correction in focal-plane arrays","volume":"38","author":"Hayat","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"San Martina, C., Torresa, S.N., and Pezoa, J.E. (2007, January 21\u201325). An effective reference-free performance metric for non-uniformity correction algorithms in infrared imaging system. Proceedings of the 20th Annual Meeting of the Laser and Electro-Optic Society, Lake Buena Vista, FL, USA.","DOI":"10.1109\/LEOS.2007.4382537"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3033\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:52:38Z","timestamp":1760122358000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3033"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,11]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063033"],"URL":"https:\/\/doi.org\/10.3390\/s23063033","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,11]]}}}