Nonlocal texture filtering with efficient tree structures and invariant patch similarity measures

Abstract: In this work, we propose a study of efficient and optimal texture denoising methods based on the nonlocal means filter. In particular, we present efficient implementations of nonlocal filtering using highly adapted data structures such as cluster trees, spill trees and cluster forests. A comparative study of computational speed indicates that cluster forests are superior to alternative methods. Moreover, we introduce several extensions of the original nonlocal means filter which introduce invariance with respect to variations in brightness, scale, and rotation.