{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:44:20Z","timestamp":1760143460699,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Slovenian Research and Innovation Agency","award":["P1-0389","J3-3083"],"award-info":[{"award-number":["P1-0389","J3-3083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The Adding-Doubling (AD) algorithm is a general analytical solution of the radiative transfer equation (RTE). AD offers a favorable balance between accuracy and computational efficiency, surpassing other RTE solutions, such as Monte Carlo (MC) simulations, in terms of speed while outperforming approximate solutions like the Diffusion Approximation method in accuracy. While AD algorithms have traditionally been implemented on central processing units (CPUs), this study focuses on leveraging the capabilities of graphics processing units (GPUs) to achieve enhanced computational speed. In terms of processing speed, the GPU AD algorithm showed an improvement by a factor of about 5000 to 40,000 compared to the GPU MC method. The optimal number of threads for this algorithm was found to be approximately 3000. To illustrate the utility of the GPU AD algorithm, the Levenberg\u2013Marquardt inverse solution was used to extract object parameters from optical spectral data of human skin under various hemodynamic conditions. With regards to computational efficiency, it took approximately 5 min to process a 220 \u00d7 100 \u00d7 61 image (x-axis \u00d7 y-axis \u00d7 spectral-axis). The development of the GPU AD algorithm presents an advancement in determining tissue properties compared to other RTE solutions. Moreover, the GPU AD method itself holds the potential to expedite machine learning techniques in the analysis of spectral images.<\/jats:p>","DOI":"10.3390\/a17020074","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T03:47:09Z","timestamp":1707277629000},"page":"74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4417-0293","authenticated-orcid":false,"given":"Matija","family":"Milanic","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Physics, University of Ljubljana, SI-1000 Ljubljana, Slovenia"},{"name":"Jozef Stefan Institute, SI-1000 Ljubljana, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rok","family":"Hren","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Physics, University of Ljubljana, SI-1000 Ljubljana, Slovenia"},{"name":"Institute of Mathematics, Physics, and Mechanics, SI-1000 Ljubljana, Slovenia"},{"name":"Syreon Research Institute, 1145 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1098\/rspl.1860.0119","article-title":"On the intensity of the light reflected from or transmitted through a pile of plates","volume":"11","author":"Stokes","year":"1862","journal-title":"Proc. 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