电子技术
電子技術
전자기술
ELECTRONIC TECHNOLOGY
2014年
4期
4-7
,共4页
图像超分辨%支持向量回归%稀疏表示
圖像超分辨%支持嚮量迴歸%稀疏錶示
도상초분변%지지향량회귀%희소표시
image super-resolution%support vector regression%sparse representation
文章提出一种新的基于支持向量回归（SVR）和稀疏表示的图像超分辨重建算法。SVR对输入数据有良好预测输出类别能力。图像统计表明，图像块可以从过完备字典中通过稀疏线性组合很好的表示。对一幅低分辨率输入图像，可以将图像超分辨问题视为在高分辨图像中估计其像素位置。与传统的支持向量回归方法相比，本文采用的特征是不同类型的图像块的稀疏表示。研究表明，稀疏表示作为特征对噪声有一定的鲁棒性。实验结果表明，本文方法与传统支持向量回归方法相比在图像重建质量上有一定的优势。
文章提齣一種新的基于支持嚮量迴歸（SVR）和稀疏錶示的圖像超分辨重建算法。SVR對輸入數據有良好預測輸齣類彆能力。圖像統計錶明，圖像塊可以從過完備字典中通過稀疏線性組閤很好的錶示。對一幅低分辨率輸入圖像，可以將圖像超分辨問題視為在高分辨圖像中估計其像素位置。與傳統的支持嚮量迴歸方法相比，本文採用的特徵是不同類型的圖像塊的稀疏錶示。研究錶明，稀疏錶示作為特徵對譟聲有一定的魯棒性。實驗結果錶明，本文方法與傳統支持嚮量迴歸方法相比在圖像重建質量上有一定的優勢。
문장제출일충신적기우지지향량회귀（SVR）화희소표시적도상초분변중건산법。SVR대수입수거유량호예측수출유별능력。도상통계표명，도상괴가이종과완비자전중통과희소선성조합흔호적표시。대일폭저분변솔수입도상，가이장도상초분변문제시위재고분변도상중고계기상소위치。여전통적지지향량회귀방법상비，본문채용적특정시불동류형적도상괴적희소표시。연구표명，희소표시작위특정대조성유일정적로봉성。실험결과표명，본문방법여전통지지향량회귀방법상비재도상중건질량상유일정적우세。
In this paper, a new approach to single-image Super Resolution(SR) based on support vector regression (SVR) with sparse representation is presented. SVR is known to offer excellent generalization ability in predicting output class labels for input data. The research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an over-complete dictionary. For a low resolution image input, we approach the SR problem as the estimation of pixel labels in its high resolution version. Compared with general SVR methods, the feature considered in this work is the sparse representation of different types of image patches. Prior studies have shown that this feature is robust to noise in image data. Experimental results show that our method is competitive or even superior in quality to images produced by conventional SVR method.
