TY - GEN
T1 - A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping
AU - Liang, Zhetong
AU - Xu, Jun
AU - Zhang, David
AU - Cao, Zisheng
AU - Zhang, Lei
PY - 2018/12/14
Y1 - 2018/12/14
N2 - Tone mapping aims to reproduce a standard dynamic range image from a high dynamic range image with visual information preserved. State-of-the-art tone mapping algorithms mostly decompose an image into a base layer and a detail layer, and process them accordingly. These methods may have problems of halo artifacts and over-enhancement, due to the lack of proper priors imposed on the two layers. In this paper, we propose a hybrid l1-l0 decomposition model to address these problems. Specifically, an l1 sparsity term is imposed on the base layer to model its piecewise smoothness property. An l0 sparsity term is imposed on the detail layer as a structural prior, which leads to piecewise constant effect. We further propose a multiscale tone mapping scheme based on our layer decomposition model. Experiments show that our tone mapping algorithm achieves visually compelling results with little halo artifacts, outperforming the state-of-the-art tone mapping algorithms in both subjective and objective evaluations.
AB - Tone mapping aims to reproduce a standard dynamic range image from a high dynamic range image with visual information preserved. State-of-the-art tone mapping algorithms mostly decompose an image into a base layer and a detail layer, and process them accordingly. These methods may have problems of halo artifacts and over-enhancement, due to the lack of proper priors imposed on the two layers. In this paper, we propose a hybrid l1-l0 decomposition model to address these problems. Specifically, an l1 sparsity term is imposed on the base layer to model its piecewise smoothness property. An l0 sparsity term is imposed on the detail layer as a structural prior, which leads to piecewise constant effect. We further propose a multiscale tone mapping scheme based on our layer decomposition model. Experiments show that our tone mapping algorithm achieves visually compelling results with little halo artifacts, outperforming the state-of-the-art tone mapping algorithms in both subjective and objective evaluations.
UR - http://www.scopus.com/inward/record.url?scp=85059631249&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2018.00500
DO - 10.1109/CVPR.2018.00500
M3 - Conference article published in proceeding or book
AN - SCOPUS:85059631249
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 4758
EP - 4766
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
PB - IEEE Computer Society
T2 - 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Y2 - 18 June 2018 through 22 June 2018
ER -