{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:39:18Z","timestamp":1760150358991,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education, Youth and Sports of the Czech Republic","award":["90254"],"award-info":[{"award-number":["90254"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This paper presents a parallel implementation of a non-local transform-domain filter (BM4D). The effectiveness of the parallel implementation is demonstrated by denoising image series from computed tomography (CT) and magnetic resonance imaging (MRI). The basic idea of the filter is based on grouping and filtering similar data within the image. Due to the high level of similarity and data redundancy, the filter can provide even better denoising quality than current extensively used approaches based on deep learning (DL). In BM4D, cubes of voxels named patches are the essential image elements for filtering. Using voxels instead of pixels means that the area for searching similar patches is large. Because of this and the application of multi-dimensional transformations, the computation time of the filter is exceptionally long. The original implementation of BM4D is only single-threaded. We provide a parallel version of the filter that supports multi-core and many-core processors and scales on such versatile hardware resources, typical for high-performance computing clusters, even if they are concurrently used for the task. Our algorithm uses hybrid parallelisation that combines open multi-processing (OpenMP) and message passing interface (MPI) technologies and provides up to 283\u00d7 speedup, which is a 99.65% reduction in processing time compared to the sequential version of the algorithm. In denoising quality, the method performs considerably better than recent DL methods on the data type that these methods have yet to be trained on.<\/jats:p>","DOI":"10.3390\/jimaging9110254","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T01:48:45Z","timestamp":1700531325000},"page":"254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Speed Up of Volumetric Non-Local Transform-Domain Filter Utilising HPC Architecture"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2631-051X","authenticated-orcid":false,"given":"Petr","family":"Strakos","sequence":"first","affiliation":[{"name":"IT4Innovations, VSB\u2014Technical University of Ostrava, 17. Listopadu 2172\/15, 708 00 Ostrava-Poruba, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4630-5339","authenticated-orcid":false,"given":"Milan","family":"Jaros","sequence":"additional","affiliation":[{"name":"IT4Innovations, VSB\u2014Technical University of Ostrava, 17. Listopadu 2172\/15, 708 00 Ostrava-Poruba, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1017-5766","authenticated-orcid":false,"given":"Lubomir","family":"Riha","sequence":"additional","affiliation":[{"name":"IT4Innovations, VSB\u2014Technical University of Ostrava, 17. Listopadu 2172\/15, 708 00 Ostrava-Poruba, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6865-1884","authenticated-orcid":false,"given":"Tomas","family":"Kozubek","sequence":"additional","affiliation":[{"name":"IT4Innovations, VSB\u2014Technical University of Ostrava, 17. Listopadu 2172\/15, 708 00 Ostrava-Poruba, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1109\/78.80892","article-title":"A Class of Fast Gaussian Binomial Filters for Speech and Image Processing","volume":"39","author":"Haddad","year":"1991","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_2","first-page":"182659","article-title":"The em method in a probabilistic wavelet-based MRI denoising","volume":"2015","author":"Villullas","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1109\/23.317385","article-title":"Three-dimensional restoration of single photon emission computed tomography images","volume":"41","author":"Boulfelfel","year":"1994","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_4","unstructured":"Buades, A., Coll, B., and Morel, J. (2005, January 20\u201326). A non-local algorithm for image denoising. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. (2006, January 16\u201318). Image denoising with block-matching and 3D filtering. Proceedings of the SPIE\u2014The International Society for Optical Engineering, San Jose, CA, USA.","DOI":"10.1117\/12.643267"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2011.04.003","article-title":"New methods for MRI denoising based on sparseness and self-similarity","volume":"16","author":"Manjon","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1002\/jmri.22003","article-title":"Adaptive non-local means denoising of MR images with spatially varying noise levels","volume":"31","author":"Manjon","year":"2010","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TMI.2007.906087","article-title":"An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images","volume":"27","author":"Coupe","year":"2008","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Coupe, P., Hellier, P., Prima, S., Kervrann, C., and Barillot, C. (2008). 3D wavelet subbands mixing for image denoising. Int. J. Biomed. Imaging, 2008.","DOI":"10.1155\/2008\/590183"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TIP.2012.2210725","article-title":"Nonlocal transform-domain filter for volumetric data denoising and reconstruction","volume":"22","author":"Maggioni","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1109\/TMI.2017.2715284","article-title":"Low-Dose CT with a residual encoder-decoder convolutional neural network","volume":"36","author":"Chen","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","unstructured":"Intel (2023, July 01). Intel\u00ae Open Image Denoise. Available online: https:\/\/www.openimagedenoise.org."},{"key":"ref_14","unstructured":"NVIDIA (2023, July 01). NVIDIA OptiX\u2122 AI-Accelerated Denoiser. Available online: https:\/\/developer.nvidia.com\/optix-denoiser."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Usui, K., Ogawa, K., Goto, M., Sakano, Y., Kyougoku, S., and Daida, H. (2021). Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography. Vis. Comput. Ind. Biomed. Art, 4.","DOI":"10.1186\/s42492-021-00087-9"},{"key":"ref_16","unstructured":"Dabov, K., Foi, A., and Egiazarian, K. (2007, January 3\u20137). Video denoising by sparse 3D transform-domain collaborative filtering. Proceedings of the 2007 15th European Signal Processing Conference, Pozna\u0144, Poland. Available online: https:\/\/webpages.tuni.fi\/foi\/GCF-BM3D\/."},{"key":"ref_17","unstructured":"Strakos, P., Jaros, M., and Karasek, T. (2017, January 30\u201331). Speed up of Volumetric Non-local Transform-Domain Filter. Proceedings of the Fifth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, Pecs, Hungary."},{"key":"ref_18","unstructured":"Cocosco, C.A., Kollokian, V., Kwan, R.K.S., and Evans, A.C. (2023, July 01). BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. NeuroImage. Available online: http:\/\/brainweb.bic.mni.mcgill.ca\/brainweb\/."},{"key":"ref_19","unstructured":"(2023, July 01). blender.org\u2014Home of the Blender Project\u2014Free and Open 3D Creation Software. Available online: https:\/\/www.blender.org\/."},{"key":"ref_20","unstructured":"(2023, July 01). MPI Forum. Available online: http:\/\/mpi-forum.org\/."},{"key":"ref_21","unstructured":"(2023, July 01). Home\u2014OpenMP. Available online: http:\/\/www.openmp.org\/."},{"key":"ref_22","unstructured":"(2023, July 01). Salomon\u2014Hardware Overview\u2014IT4Innovations Documentation. Available online: https:\/\/docs.it4i.cz\/salomon\/hardware-overview\/."},{"key":"ref_23","unstructured":"(2023, July 01). Anselm\u2014Hardware Overview\u2014IT4Innovations Documentation. Available online: https:\/\/docs.it4i.cz\/anselm\/hardware-overview\/."},{"key":"ref_24","unstructured":"(2023, July 01). HLRN Website. Available online: https:\/\/www.hlrn.de\/supercomputer-e\/hlrn-iii-system\/."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/11\/254\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:29Z","timestamp":1760131529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/11\/254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,20]]},"references-count":24,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["jimaging9110254"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9110254","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2023,11,20]]}}}