A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution
A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution
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DOI：
10.1109/tip.2014.2305844
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发表时间：
2014-06-01
影响因子：
10.6
通讯作者：
Elad, Michael
中科院分区：
文献类型：
--
作者：
Peleg, Tomer;Elad, Michael
我们使用基于低分辨率和高分辨率图像补丁的稀疏表示的统计预测模型来解决单个图像超分辨率问题。建议的模型使我们能够避免任何不变性假设，这是一个常见的做法，在稀疏的方法来处理这个任务。通过MMSE估计获得高分辨率补丁的预测，并且所得方案具有前向神经网络的有用解释。为了进一步提高性能，我们提出了数据聚类和级联几个层次的基本算法。我们提出了一个训练方案，由此产生的网络和证明我们的算法的能力，显示其优势，现有的方法的基础上，低和高分辨率的字典对，在计算复杂性，数值标准，和视觉外观。所建议的方法提供了一个理想的折衷之间的低计算复杂性和重建质量，当比较它与国家的最先进的方法为单图像超分辨率。
We address single image super-resolution using a statistical prediction model based on sparse representations of low-and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low-and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.