A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping

Zhetong Liang, Jun Xu, David Zhang, Zisheng Cao, Lei Zhang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4758-4766

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Liang_2018_CVPR,
author = {Liang, Zhetong and Xu, Jun and Zhang, David and Cao, Zisheng and Zhang, Lei},
title = {A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}