Deblurring of point cloud attributes in graph spectral domain
Kaoru Yamamoto, Masaki Onuki, Yuichi Tanaka · 2016
We propose a deblurring algorithm of point cloud attributes inspired by multi-Wiener SURE-LET deconvolution. The image reconstructed by the SURE-LET approach is expressed as a linear combination of multiple filtered images by the filters defined on the frequency domain. The coefficients of the linear combination are calculated so that the estimate of mean squared error between the original and restored images is minimized. However, since the SURE-LET approach is only adjusted to images, it cannot directly be applied to point cloud attributes, e.g., texture data on 3D models, since they cannot be transformed to their frequency domain. To overcome the problem, we use graph signal processing (GSP) for deblurring the complex-structured data. That is, the SURE-LET approach is redefined on GSP, where the Wiener-like filtering is followed by the subband decomposition with an analysis graph filter bank, and then thresholding for each subband is performed. In the experiments, the proposed method is applied to blurred textures on 3D models, and the experimental results show clearly deblurred textures on a 3D model and present good SNRs.