CAJ | 학술논문

针对现有基于样本学习的人脸超分辨率算法对人脸图像采用全局搜索，存在非局部误匹配且复原图像视觉效果不佳等问题，提出了一种新的基于匹配学习的人脸图像超分辨率算法。首先根据输入图像预分类得到一个样本子类库，并构建相应的特征图像。在匹配过程中，针对不同人脸图像，采用2种新的搜索策略，考虑了图像块之间的相似性和一致性，使复原图像看起来更加连贯自然。实验结果表明，与其他方法相比，本文算法生成的高分辨率人脸图像获得了更好的视觉效果和更高的平均峰值信噪比，具有很好的实用价值。
침대현유기우양본학습적인검초분변솔산법대인검도상채용전국수색，존재비국부오필배차복원도상시각효과불가등문제，제출료일충신적기우필배학습적인검도상초분변솔산법。수선근거수입도상예분류득도일개양본자류고，병구건상응적특정도상。재필배과정중，침대불동인검도상，채용2충신적수색책략，고필료도상괴지간적상사성화일치성，사복원도상간기래경가련관자연。실험결과표명，여기타방법상비，본문산법생성적고분변솔인검도상획득료경호적시각효과화경고적평균봉치신조비，구유흔호적실용개치。
The exsiting example-based super-resolution algorithms of face image adopt global search, which causes the problems of non-local mismatch and poor visual effect of image restoration. A new matching and learning-based face image super-resolution restoration algorithm is proposed. A pre-classification process of input image is applied to get a sub-sample library from the image library, and the corresponding feature images are created. In the matching process, two new search strategies for different face images are used, which consider the similarity and consistency between image patches and make the recovered image look more coherent and natural. Experimental results show that the proposed algorithm synthesizes high-resolution faces with better visual effect and obtains higher values of the average of Peak Signal-to-Noise Ratios(PSNR) when compared with other methods.