L_0 gradient minimization can be applied to an input signal to control the number of non-zero gradients. This is useful in reducing small gradients generally associated with signal noise, while preserving important signal features. In computer vision, L_0 gradient minimization has found applications in image denoising, 3D mesh denoising, and image enhancement. Minimizing the L_0 norm, however, is an NP-hard problem because of its non-convex property. As a result, existing methods rely on approximation strategies to perform the minimization. In this paper, we present a new method to perform L_0 gradient minimization that is fast and effective. Our method uses a descent approach based on region fusion that converges faster than other methods while providing a better approximation of the optimal L_0 norm. In addition, our method can be applied to both 2D images and 3D mesh topologies. The effectiveness of our approach is demonstrated on a number of examples.
Please download the newest version C++ code from here. In this new version, a local return pointer bug is fixed for the 3D mesh denoising. We thanks Evgeny Levinkov from the Max Planck Institute of Informatics, Germany for helping us point it out.
|Rang NGUYEN||nguyenho at comp.nus.edu.sg|
|Michael S. BROWN||brown at comp.nus.edu.sg|
Last updated: 10 March 2016