A Novel Edit Propagation Algorithm via L0 Gradient Minimization

In this paper, we study how to perform edit propagation using L-0 gradient minimization. Existing propagation methods only take simple constraints into consideration and neglects image structure information. We propose a new optimization framework making use of L-0 gradient minimization, which can globally satisfy user-specified edits as well as tackle counts of non-zero gradients. In this process, a modified affinity matrix approximation method which efficiently reduces randomness is raised. We introduce a self-adaptive re-parameterization way to control the counts based on both original image and user inputs. Our approach is demonstrated by image recoloring and tonal values adjustments. Numerous experiments show that our method can significantly improve edit propagation via L-0 gradient minimization.