Image denoising by sparse 3-D transform-domain collaborative filtering
Image denoising by sparse 3-D transform-domain collaborative filtering
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DOI：
10.1109/tip.2007.901238
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发表时间：
2007-08-01
影响因子：
10.6
通讯作者：
Egiazarian, Karen
中科院分区：
文献类型：
--
作者：
Dabov, Kostadin;Foi, Alessandro;Egiazarian, Karen
我们提出了一种新的图像去噪策略的基础上，在变换域的增强稀疏表示。稀疏性的增强是通过将相似的2-D图像片段（例如，块）成三维数据阵列，我们称之为“组”。“协同过滤是一个特殊的程序开发来处理这些三维组。我们使用三个连续的步骤来实现它：一个组的3-D变换，变换谱的收缩，和逆3-D变换。结果是由联合滤波的分组图像块组成的3-D估计。通过衰减噪声，协同过滤揭示了分组块共享的最细微的细节，同时保留了每个单独块的基本独特特征。然后将过滤后的块返回到其原始位置。由于这些块是重叠的，因此对于每个像素，我们会获得许多需要组合的不同估计。聚合是一种特殊的平均过程，它被用来利用这种冗余。一个显着的改善是通过一个专门开发的协同维纳滤波。基于这种新的去噪策略及其有效的实现算法的全部细节，扩展到彩色图像去噪也开发。实验结果表明，该算法在峰值信噪比和主观视觉质量方面都达到了最先进的去噪性能。
We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2-D image fragments (e.g., blocks) into 3-D data arrays which we call "groups." Collaborative filtering is a special procedure developed to deal with these 3-D groups. We realize it using the three successive steps: 3-D transformation of a group, shrinkage of the transform spectrum, and inverse 3-D transformation. The result is a 3-D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.