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Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation.

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Ovidiu Dan Banica

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Real-time retinex-based and learning-based methods for computational color constancy with high accuracy ; Metode visoke točnosti za računalnu postojanost boja za rad u stvarnom vremenu temeljene na Retinex pristupu te učenju.. University of Zagreb, Croatia, (2016)base-search.net (ftnulzagrebdr:oai:dr.nsk.hr:fer_4867).Towards hardware-friendly retinex algorithms., и . ISPA, стр. 104-108. IEEE, (2017)Color Rabbit: Guiding the distance of local maximums in illumination estimation., и . DSP, стр. 345-350. IEEE, (2014)Color Dog - Guiding the Global Illumination Estimation to Better Accuracy., и . VISAPP (1), стр. 129-135. SciTePress, (2015)Color Beaver: Bounding Illumination Estimations for Higher Accuracy., , и . VISIGRAPP (4: VISAPP), стр. 183-190. SciTePress, (2019)Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source., и . VISIGRAPP (4: VISAPP), стр. 191-197. SciTePress, (2019)The Cube++ Illumination Estimation Dataset., , , , , , , , и . CoRR, (2020)TVOR: Finding Discrete Total Variation Outliers among Histograms., и . CoRR, (2020)Flash, Storm, and Mistral: Hardware-Friendly and High Quality Tone Mapping., и . VISIGRAPP (Revised Selected Papers), том 997 из Communications in Computer and Information Science, стр. 228-242. Springer, (2018)Make Potatoes Great Again., , , , и . ISPA, стр. 82-88. IEEE, (2019)