摘要
为了充分利用图像的自相似性质,针对图像的超分辨率重建问题构造了一个多任务学习的问题,并基于高斯过程回归进一步扩展,提出了一个新颖的单帧图像超分辨率重建算法.该算法能够在图像的超分辨率重建结果中有效地抑制图像显著边缘处出现的噪声和伪影,并能生成视觉效果更为自然的高分辨率图像.实验表明该算法产生的高分辨率图像结果能够相当于甚至超越当前先进的算法.
For making full use of the property of self similarity,a novel SISR method based on a variant of Gaussian process regression( GPR) was proposed by considering it as a multi-task learning problem.This method could efficiently suppress the artifacts along the salient edges and produce natural looking appearance in the final high resolution( HR) results. Extensive experimental results demonstrated that the results produced by our method could be equivalent or superior to other state of the art algorithms.
出处
《仲恺农业工程学院学报》
CAS
2015年第4期50-53,共4页
Journal of Zhongkai University of Agriculture and Engineering
基金
Guangdong province special fund research project for scientific and professional construction of university(2013WYXM0058)
Guangdong province science and technology planning project(2013B020314019)
关键词
单帧图像超分辨率
多任务学习
高斯过程回归
自相似
single image super-resolution
multi-task learning
gaussian process regression
self-similarity