中文摘要：

基于实例的图像超分辨率方法通过已知实例图像学习高低分辨率图像之间的关系模型, 利用该模型预测未知高分辨率图像信息, 具有较好的放大效果, 但需要庞大的外部图像库. 为此, 提出一种特征约束的多实例图像超分辨率方法. 首先提出特征约束多项式插值方法初始化高分辨率低频图像; 其次以高、低分辨率图像的低频图像作为已知实例对, 在低分辨率低频图像中, 对高分辨率低频图像块采用自适应KNN 搜索算法搜索相似图像块并得出回归关系模型; 最后将该模型应用到低分辨率高频图像获取初始高分辨率图像所缺失的高频信息. 大量实验结果表明, 该方法产生的高分辨率图像可以较好地保持图像特征, 具有较高的PSNR 值及SSIM 值.

英文摘要：

Example-based super-resolution algorithm predicts unknown high-resolution image information by the relationship model learnt from the known high- and low-resolution image pairs. This kind of algo-rithm can produce high-quality images, but relies on large extern image database. We propose a multi- example based image super-resolution method constrained by image features. First, our method initially high-resolves the low-resolution image by the proposed feature-constrained polynomial interpolation method. Second, we consider low-frequency versions of high- and low-resolution images as the example pair. Each patch in the high-resolution low-frequency image searches its similar patches from the low-resolution image by adaptiveKNN search algorithm, and the regression model between similar patches are learnt. Finally, the learnt model is applied to low-resolution low-frequency image to complement high-resolution high-frequency information. Extensive experiments show that the proposed method produces high-quality high-resolution images with high PSNR and SSIM values.