SRHRF+:使用分层随机森林增强单幅图像超分辨率的自我示例

Jun-Jie Huang, Tian-Rui Liu, P. Dragotti, T. Stathaki
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引用次数: 23

摘要

基于示例的单幅图像超分辨率(SISR)方法使用外部训练数据集,最近引起了人们的广泛关注。基于自示例的SISR方法利用了自然图像中冗余的非局部自相似模式,因此更能适应手头的图像,从而生成高质量的超分辨率图像。在本文中,我们提出将基于示例的SISR和基于自示例的SISR的优势结合起来。提出了一种基于分层随机森林的超分辨率(SRHRF)方法,从外部训练图像中学习统计先验。随机森林的每一层通过聚合来自多个决策树的预测模型来减少由于方差引起的估计误差。分层结构通过将由于偏差引起的估计误差推向零进一步提高了性能。为了进一步自适应改进超分辨图像,从下采样SRHRF生成的图像金字塔对中学习自例随机森林(SERF)。大量的数值结果表明,使用SERF (SRHRF+)增强的SRHRF方法在自然图像上达到了最先进的性能,并且在具有丰富自相似模式的图像上产生了显著的优异性能。
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SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests
Example-based single image super-resolution (SISR) methods use external training datasets and have recently attracted a lot of interest. Self-example based SISR methods exploit redundant non-local self-similar patterns in natural images and because of that are more able to adapt to the image at hand to generate high quality super-resolved images. In this paper, we propose to combine the advantages of example-based SISR and self-example based SISR. A novel hierarchical random forests based super-resolution (SRHRF) method is proposed to learn statistical priors from external training images. Each layer of random forests reduce the estimation error due to variance by aggregating prediction models from multiple decision trees. The hierarchical structure further boosts the performance by pushing the estimation error due to bias towards zero. In order to further adaptively improve the super-resolved image, a self-example random forests (SERF) is learned from an image pyramid pair constructed from the down-sampled SRHRF generated result. Extensive numerical results show that the SRHRF method enhanced using SERF (SRHRF+) achieves the state-of-the-art performance on natural images and yields substantially superior performance for image with rich self-similar patterns.
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