Bidirectional texture function image super-resolution using singular value decomposition

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 super-resolution (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 single-image 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.