Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
复制标题
通过自适应稀疏域选择和自适应正则化进行图像去模糊和超分辨率
DOI：
10.1109/tip.2011.2108306
复制
发表时间：
2011-07-01
影响因子：
10.6
通讯作者：
Wu, Xiaolin
中科院分区：
文献类型：
--
作者：
Dong, Weisheng;Zhang, Lei;Wu, Xiaolin
稀疏表示作为一种强有力的统计图像建模技术，已成功地应用于各种图像恢复应用中。稀疏表示的成功归功于l1范数优化技术的发展和自然图像在某些领域本质上是稀疏的这一事实。图像恢复的质量在很大程度上取决于所采用的稀疏域是否能很好地代表底层图像。考虑到内容可以在不同的图像或不同的补丁在一个单一的图像显着变化，我们建议学习各种集的基地从预先收集的数据集的例子图像补丁，然后，对于一个给定的补丁处理，一组基地自适应地选择来表征局部稀疏域。我们进一步引入两个自适应正则化项到稀疏表示框架中。首先，从示例图像块的数据集学习一组自回归（AR）模型。自适应地选择最适合给定块的AR模型来正则化图像局部结构。其次，图像的非局部自相似性被引入作为另一个正则化项。此外，稀疏正则化参数的自适应估计，以更好的图像恢复性能。大量的图像去模糊和超分辨率实验表明，通过自适应稀疏域选择和自适应正则化，该方法在PSNR和视觉感知方面都取得了比现有算法更好的效果。
As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the l1-norm optimization techniques and the fact that natural images are intrinsically sparse in some domains. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a precollected dataset of example image patches, and then, for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image nonlocal self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.