Edge-Preserving Decompositions for Multi-Scale with Adaptive TV
Zhiqiang Cui, Zhixun Su · 2014
Digital image processing has received widespread attention in recent years, multi-scale image decomposition has been extensively studied.Many recent computational photography techniques decompose an image into a smooth base layer, and a residual detail layer.In many applications, it is important to control the spatial scale of the extracted details.Aiming at this problem, we have extensively studied the weighted least squares framework firstly, through the image priors total variation to preserve the edge; Second, the paper develop an adaptive weight function that is able to describe some structures of an image.This adaptive weight function together with TV regularizer can able preserve large scale structures and remove tiny details.Finally, the proposed method is solved by alternating iterative minimization, which has fast convergence peroperty.Experiments show that our method can keep the image edges and avoid visual artifacts in image processing.Moreover, our method has obvious advantages in operation time.It is efficient algorithm to extracted details at the arbitrary multi-scale.