Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68387
Title: Bidirectional texture function image super-resolution using singular value decomposition
Authors: Dong, W
Shen, HL
Pan, ZW
Xin, JH 
Issue Date: 2017
Publisher: Optical Society of America
Source: Applied optics, 2017, v. 56, no. 10, p. 2745-2753 How to cite?
Journal: Applied optics 
Abstract: The bidirectional texture function (BTF) is widely employed to achieve realistic digital reproduction of real-world material appearance. In practice, a BTF measurement device usually does not use high-resolution (HR) cameras in data collection, considering the high equipment cost and huge data space required. The limited image resolution consequently leads to the loss of texture details in BTF data. This paper proposes a fast BTF image superresolution (SR) algorithm to deal with this issue. The algorithm uses singular value decomposition (SVD) to separate the collected low-resolution (LR) BTF data into intrinsic textures and eigen-apparent bidirectional reflectance distribution functions (eigen-ABRDFs) and then improves the resolution of the intrinsic textures via image SR. The HR BTFs can be finally obtained by fusing the reconstructed HR intrinsic textures with the LR eigen-ABRDFs. Experimental results show that the proposed algorithm outperforms the state-of-the-art singleimage SR algorithms in terms of reconstruction accuracy. In addition, thanks to the employment of SVD, the proposed algorithm is computationally efficient and robust to noise corruption.
URI: http://hdl.handle.net/10397/68387
ISSN: 1559-128X
EISSN: 2155-3165
DOI: 10.1364/AO.56.002745
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