Nurcan SeylanSerkan ErgunAydin ÖztürkSeylan, NurcanErgun, SerkanOzturk, Aydin2025-10-06201397880869437492-s2.0-84884171023https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884171023&partnerID=40&md5=b8ab6db6ca6dd1cccf2b396228d89884https://gcris.yasar.edu.tr/handle/123456789/10079Compressive sensing is a technique for efficiently acquiring and reconstructing the data. This technique takes advantage of sparseness or compressibility of the data allowing the entire measured data to be recovered from relatively few measurements. Considering the fact that the BRDF data often can be highly sparse we propose to employ the compressive sensing technique for an efficient reconstruction. We demonstrate how to use compressive sensing technique to facilitate a fast procedure for reconstruction of large BRDF data. We have showed that the proposed technique can also be used for the data sets having some missing measurements. Using BRDF measurements of various isotropic materials we obtained high quality images at very low sampling rates both for diffuse and glossy materials. Similar results also have been obtained for the specular materials at slightly higher sampling rates. © 2013 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessBrdf Reconstruction, Compressive Sensing, Compressive Sensing, Efficient Reconstruction, High Quality Images, Isotropic Materials, Measurements Of, Missing Measurements, Sampling Rates, Specular Materials, Computer Graphics, Sensors, Visualization, Signal ReconstructionCompressive sensing, Efficient reconstruction, High quality images, Isotropic materials, Measurements of, Missing measurements, Sampling rates, Specular materials, Computer graphics, Sensors, Visualization, Signal reconstructionBRDF ReconstructionCompressive SensingConference Object