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基于 QoE 的分层多元高斯 CRF 中的多曝光融合。

QoE-based multi-exposure fusion in hierarchical multivariate Gaussian CRF.

机构信息

Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.

出版信息

IEEE Trans Image Process. 2013 Jun;22(6):2469-78. doi: 10.1109/TIP.2012.2236346.

Abstract

Many state-of-the-art fusion methods, combining details in images taken under different exposures into one well-exposed image, can be found in the literature. However, insufficient study has been conducted to explore how perceptual factors can provide viewers better quality of experience on fused images. We propose two perceptual quality measures: perceived local contrast and color saturation, which are embedded in our novel hierarchical multivariate Gaussian conditional random field model, to illustrate improved performance for multi-exposure fusion. We show that our method generates images with better quality than existing methods for a variety of scenes.

摘要

许多最先进的融合方法,将不同曝光下拍摄的图像细节融合到一张曝光良好的图像中,在文献中都有介绍。然而,对于感知因素如何为融合图像的观察者提供更好的体验质量,还没有进行足够的研究。我们提出了两种感知质量度量:感知局部对比度和颜色饱和度,将其嵌入到我们新颖的分层多元高斯条件随机场模型中,以说明多曝光融合的性能得到了提高。我们表明,我们的方法生成的图像质量优于各种场景下现有的方法。

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