Super-resolution image restoration algorithm based on SVR and PCA
基于SVR和PCA的超分辨率图像恢复算法应用研究

Using a single low resolution image to reconstruct a super-resolution image, usually based on the sample image reconstruction, but this kind of algorithm efficiency is low. This paper presented a SVR and PCA based image restoration method. Firstly, it decomposed the low resolution images into several small pieces and projected these samples onto a smaller space. Then trained the SVR using these samples and their corresponding high resolution patches. During the restoration procedure, it decomposed the test image in the same way and project using trained PCA model. After SVR, mapped each low resolution patch to a high resolution patch which was used to restore the final image. The experiments show that the method can achieve better performance than cubic interpolation method and also has very high computational efficiency.