Sparse Orthonormal Transforms (SOT) has recently been proposed as a data compression method that can achieve sparser representations in transform domain. Given initial conditions,...
In this paper, we propose a new and powerful shape denoising technique for processing surfaces approximated by triangle meshes and soups. Our approach is inspired by recent non-lo...
Shin Yoshizawa, Alexander G. Belyaev, Hans-Peter S...
This paper introduces a novel stochastic approach to image denoising using an adaptive Monte Carlo scheme. Random samples are generated from the image field using a spatially-adap...
Alexander Wong, Akshaya Kumar Mishra, Paul W. Fieg...
A no-reference image metric based on the singular value decomposition of local image gradients is proposed in this paper. This metric provides a quantitative measure of true image...
In this paper, we propose a novel image denoising method by incorporating the dual-tree complex wavelets into the ordinary ridgelet transform. The approximate shift invariant prop...