ADAPTIVE NOISE SMOOTHING FILTER FOR IMAGES WITH SIGNAL-DEPENDENT NOISE
ADAPTIVE NOISE SMOOTHING FILTER FOR IMAGES WITH SIGNAL-DEPENDENT NOISE
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DOI：
10.1109/tpami.1985.4767641
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发表时间：
1985-01-01
影响因子：
23.6
通讯作者：
CHAVEL, P
中科院分区：
文献类型：
--
作者：
KUAN, DT;SAWCHUK, AA;CHAVEL, P
在本文中，我们考虑的恢复与信号相关的噪声的图像。该滤波器是噪声平滑和适应局部变化的图像统计的基础上的非平稳均值，非平稳方差（NMNV）图像模型。对于由一类不相关的、无模糊的信号相关噪声退化的图像，自适应噪声平滑滤波器成为点处理器，并且类似于Lee的局部统计算法[16]。该滤波器能够适应非平稳局部图像统计在不同类型的信号相关的噪声的存在下。对于乘性噪声，自适应噪声平滑滤波器是Lee算法的系统推导，具有允许局部图像方差的不同估计的一些扩展。推导的优点是其易于扩展，以处理各种类型的信号相关的噪声。还考虑了薄膜颗粒和泊松信号相关的恢复问题作为示例。该滤波器所需的所有非平稳图像统计参数都可以从含噪图像中估计出来，而不需要原始图像的先验信息。
In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.