Dong Wei, Shen Hui-Liang, Pan Zhi-Wei, Xin John H
Appl Opt. 2017 Apr 1;56(10):2745-2753. doi: 10.1364/AO.56.002745.
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.
双向纹理函数（BTF）被广泛用于实现对真实世界材料外观的逼真数字再现。在实际应用中，考虑到设备成本高和所需的数据空间巨大，BTF测量设备在数据采集时通常不使用高分辨率（HR）相机。因此，有限的图像分辨率导致BTF数据中纹理细节的丢失。本文提出了一种快速BTF图像超分辨率（SR）算法来处理这一问题。该算法使用奇异值分解（SVD）将采集到的低分辨率（LR）BTF数据分离为固有纹理和本征表观双向反射分布函数（本征ABRDF），然后通过图像超分辨率提高固有纹理的分辨率。最终，通过将重建的高分辨率固有纹理与低分辨率本征ABRDF融合，可以获得高分辨率BTF。实验结果表明，该算法在重建精度方面优于现有单图像超分辨率算法。此外，由于采用了奇异值分解，该算法计算效率高，对噪声干扰具有鲁棒性。
