基于自相似性与多任务高斯过程回归的单帧图像超分辨率重建

李键红; 吴亚榕; 吕巨建

doi:10.3788/OPE.20182611.2814

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基于自相似性与多任务高斯过程回归的单帧图像超分辨率重建

信息科学 | 更新时间：2020-07-05

- 基于自相似性与多任务高斯过程回归的单帧图像超分辨率重建
- Single image super-resolution reconstruction algorithm based on image self-similarity and multi-task Gaussian process regression
- 光学精密工程 2018年26卷第11期页码：2814-2826
- 作者机构：
  
  1.广东外语言外贸大学语言工程与计算实验, 广东广州 510006
  2.仲恺农业工程学院仲恺科技服务公司, 广东广州 510225
  3.广东技术师范学院计算机学院, 广东广州 510665
- 作者简介：
  
  [ "李键红(1981-), 男, 辽宁朝阳人, 博士, 硕士生导师, 助理研究员, 2016年于中山大学获得博士学位, 主要从事图像处理、计算机辅助语言学习等方向的研究。E-mail:lijianhonghappy@163.com" ]
  [ "吴亚榕(1981-), 女, 湖南常德人, 助理研究员, 2011年于广东工业大学获得硕士学位, 主要从事图像处理、科技成果转化评估算法等方向的研究。E-mail:wyrljh@163.com" ]
- 基金信息：
  
  广东省自然科学基金资助项目(2017A030310618);广东省科技计划资助项目(2016A020210131);广东外语外贸大学语言工程与计算实验室项目(LEC2016ZBK004)
- DOI：10.3788/OPE.20182611.2814
  中图分类号： TP391.4
- 收稿日期：2018-05-30，
  
  录用日期：2018-6-25，
  
  纸质出版日期：2018-11-25
- 稿件说明：
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李键红, 吴亚榕, 吕巨建. 基于自相似性与多任务高斯过程回归的单帧图像超分辨率重建[J]. 光学精密工程, 2018,26(11):2814-2826.

Jian-hong LI, Ya-rong WU, Ju-jian LÜ. Single image super-resolution reconstruction algorithm based on image self-similarity and multi-task Gaussian process regression[J]. Optics and precision engineering, 2018, 26(11): 2814-2826.
李键红, 吴亚榕, 吕巨建. 基于自相似性与多任务高斯过程回归的单帧图像超分辨率重建[J]. 光学精密工程, 2018,26(11):2814-2826. DOI： 10.3788/OPE.20182611.2814.

Jian-hong LI, Ya-rong WU, Ju-jian LÜ. Single image super-resolution reconstruction algorithm based on image self-similarity and multi-task Gaussian process regression[J]. Optics and precision engineering, 2018, 26(11): 2814-2826. DOI： 10.3788/OPE.20182611.2814.

摘要

在单帧图像超分辨率问题中，基于高斯过程回归的超分辨率算法没有挖掘相似图像片间的关联关系或者无差别地用相似图像片来扩充训练集合，都会导致重建的高分辨率图像中存在明显的噪声和伪影。对此提出了一种基于多任务高斯过程回归的超分辨率算法。该算法通过引入多任务学习思想，将输入的低分辨率图像进行分片处理，把每一个图像片的超分辨率过程视为一个任务。在对相似任务建模的过程中，通过最优化求解的参数集合来体现任务间的共性及差异，从而使模型的泛化能力和预测精度得以提高，在重建高分辨率图像清晰锐利的同时，噪声和伪影受到明显抑制。用常见的测试图像以及公开的图像测试集合进行的大量试验表明该算法在主观评价和客观评价两个方面均优于同类型算法及当前经典算法，峰值信噪比较其它常见超分辨率算法可提高约0.5 dB。

Abstract

In the domain of single image super-resolution

algorithms based on Gaussian process regression neither exploit the association relationships among similar patches

nor do they discriminate between these patches with similar properties to augment the volume of the training set

which leads to obvious noise and artifacts in reconstructed high-resolution images. To overcome this problem

a new super-resolution algorithm based on multi-task Gaussian process regression is proposed. This algorithm introduces the idea of multi-task learning to partition the input low-resolution image into overlapped patches and considers the super-resolution process of each patch as a task. In the process of modeling similar tasks

the parameter set obtained by optimal solving for representing the commonness and difference gives generalization ability

improves prediction accuracy improved

makes the reconstructed high-resolution image clear and sharp

and suppresses noise and artifacts significantly. A large number of experiments to process common testing images and a public image test set subjectively and objectively demonstrate that this algorithm is superior to similar state of the art algorithms

and the peak signal to noise ratio is approximately 0.5 dB higher than that of other common super-resolution algorithms.

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Keywords

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