基于词典学习和稀疏表示的超分辨率方法

近年来，从大规模数据集中提取过完备词典，并使用稀疏表示在图像去噪、图像去马赛克和图像修复中有着较广泛应用。然而，这一技术不能直接用于处理具有异构特点的低分辨率/高分辨率图像块对，以及相应的图像超分辨率重构。要解决这一问题，文中提出一种求解同时满足两个过完备词典(低分辨率图像块词典和高分辨率图像块词典)下的相同稀疏表示的方法，并利用它们实现图像稀疏表示的超分辨率重建。为了进一步提高彩色图像的超分辨率效果，还提出基于超分辨率亮度信息的UV色度超分辨率重构。实验结果表明文中方法无论在视觉效果还是均方根误差上都获得更好结果。

Super-Resolution through Dictionary Learning and Sparse Representation

The overcomplete dictionary extracted from large scale dataset and sparse representation have been widely applied in image denoise, deblocking and inpainting in recent years. However, this technique can not be directly employed to deal with heterogeneous low resolution and high resolution image patches and relevant image reconstruction with super-resolution as well. The method to yield the sparse representation meeting two overcomplete dictionaries of different scales at the same time is proposed in this paper and the super-resolution reconstruction of image sparse representation is implemented by it. To further improve the super-resolution effect of color images, the UV chroma super-resolution reconstruction based on super-resolution luminance information is put forward as well. The experimental results show the method in this paper obtain better outcome no matter in visual effects or in root mean squared (RMS) error.