@article{Shao2015, 
author = {Pan Shao and Shouhong Ding and Lizhuang Ma and Yunsheng Wu and Yongjian Wu},
title = {Edge-preserving image decomposition via joint weighted least squares},
year = {2015},
journal = {Computational Visual Media},
volume = {1},
number = {1},
pages = {37-47},
keywords = {detail suppression, edge extraction, edge-preserving decomposition, shape recovery},
url = {https://www.sciopen.com/article/10.1007/s41095-015-0006-4},
doi = {10.1007/s41095-015-0006-4},
abstract = {Recent years have witnessed the emergence of image decomposition techniques which effectively separate an image into a piecewise smooth base layer and several residual detail layers. However, the intricacy of detail patterns in some cases may result in side-effects including remnant textures, wrongly-smoothed edges, and distorted appearance. We introduce a new way to construct an edge-preserving image decomposition with properties of detail smoothing, edge retention, and shape fitting. Our method has three main steps: suppressing high-contrast details via a windowed variation similarity measure, detecting salient edges to produce an edge-guided image, and fitting the original shape using a weighted least squares framework. Experimental results indicate that the proposed approach can appropriately smooth non-edge regions even when textures and structures are similar in scale. The effectiveness of our approach is demonstrated in the contexts of detail manipulation, HDR tone mapping, and image abstraction.}
}