Ensemble Super-Resolution With a Reference Dataset
Ensemble Super-Resolution With a Reference Dataset
复制标题
具有参考数据集的集成超分辨率
DOI：
10.1109/tcyb.2018.2890149
复制
发表时间：
2020-11-01
影响因子：
11.8
通讯作者：
Ma, Jiayi
中科院分区：
文献类型：
--
作者：
Jiang, Junjun;Yu, Yi;Ma, Jiayi
通过开发复杂的图像先验或设计深度(ER)结构，最近提出了各种图像超分辨率(SR)方法，并取得了非常好的性能。一个自然出现的问题是，这些方法是否可以被重新表述为一个统一的框架，以及这个框架是否有助于SR重建？本文提出了一种简单而有效的基于集成学习的单幅图像超分辨率方法，其性能优于任何一种待集成的超分辨率方法(或称为分量超分辨率)。基于更好的分量超解析器在进行随机共振重建时应该具有更大的集成权重的假设，我们提出了一种最大后验概率(MAP)估计框架来推断最优集成权重。特别是，我们引入了一个由高分辨率(HR)和低分辨率(LR)图像对组成的参考数据集来衡量不同分量超分辨率图像的SR能力(先验知识)。为了得到最优的集成权重，我们建议在MAP估计框架中加入重构约束，即退化的HR估计应该等于LR观测估计，以及集成权重的先验知识。此外，所提出的优化问题可以用解析解来求解。通过比较现有的四种基于非深度学习的方法、四种最新的深度学习方法和一种基于集成学习的方法，研究了该方法的性能，并在一些普通图像数据集和人脸图像数据集上证明了该方法的有效性和优越性。
By developing sophisticated image priors or designing deep(er) architectures, a variety of image super-resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers). Based on the assumption that better component super-resolver should have larger ensemble weight when performing SR reconstruction, we present a maximum a posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Especially, we introduce a reference dataset, which is composed of high-resolution (HR) and low-resolution (LR) image pairs, to measure the SR abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR estimation should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art nondeep learning-based methods, four latest deep learning-based methods, and one ensemble learning-based method, and prove its effectiveness and superiority on some general image datasets and face image datasets.