论文标题
基于斑块的图像超级分辨率使用通用高斯混合模型
Patch-based image Super Resolution using generalized Gaussian mixture model
论文作者
论文摘要
单图像超级分辨率（SISR）方法旨在从低分辨率观测值中恢复高分辨率的清洁图像。基于斑块的方法家族受到了相当大的关注和发展。最小均方根误差（MMSE）方法是一种强大的图像恢复方法，该方法在图像贴片上使用概率模型。本文提出了一种算法，以从一对低分辨率贴片和参考数据中的相应的高分辨率贴片中学习联合通用的高斯混合模型（GGMM）。然后，我们基于MMSE方法重建高分辨率图像。我们的数值评估表明，themmse-ggmm方法与其他最先进的方法竞争。
Single Image Super Resolution (SISR) methods aim to recover the clean images in high resolution from low resolution observations.A family of patch-based approaches have received considerable attention and development. The minimum mean square error (MMSE) methodis a powerful image restoration method that uses a probability model on the patches of images. This paper proposes an algorithm to learn a jointgeneralized Gaussian mixture model (GGMM) from a pair of the low resolution patches and the corresponding high resolution patches fromthe reference data. We then reconstruct the high resolution image based on the MMSE method. Our numerical evaluations indicate that theMMSE-GGMM method competes with other state of the art methods.
