Signal dependent noise removal from a single image
Signal dependent noise removal from a single image
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DOI：
10.1109/icip.2014.7025542
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Xinhao Liu;Masayuki Tanaka;M. Okutomi
中科院分区：
文献类型：
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作者：
Xinhao Liu;Masayuki Tanaka;M. Okutomi
现有的图像去噪算法通常假设加性白色高斯噪声（AWGN），尽管它们已经取得了优异的性能，但建模和去除单个图像中的真实的信号相关噪声仍然是一个具有挑战性的问题。针对信号相关噪声，提出了一种基于分割的图像去噪算法。通过对噪声识别算法的分析，我们将这两个模块集成为一个针对信号相关噪声的全盲端到端去噪算法。首先，我们确定一个给定的单个噪声图像的噪声水平函数。然后，在初始去噪之后，对预滤波图像应用分割。假设每一段的噪声水平是恒定的，我们应用AWGN去噪算法对每一段。我们得到一个最终的去噪图像，通过组成的去噪段。在合成图像和真实的噪声图像上的各种实验结果表明，我们的算法在去除真实的信号相关噪声方面优于现有的去噪算法。
State-of-the-art image denoising algorithms usually assume additive white Gaussian noise (AWGN), although they have achieved outstanding performance, modeling and removing real signal dependent noise from a single image still remains a challenging problem. In this paper we propose a segmentation-based image denoising algorithm for signal dependent noise. Incorporating a noise identification algorithm, we integrate these two modules into a full blind, end-to-end denoising algorithm for signal dependent noise. First, we identify the noise level function for a given single noisy image. Then, after initial denoising, segmentation is applied to the pre-filtered image. Assuming the noise level of each segment is constant, we apply AWGN denoising algorithm to each segment. We obtain a final de-noised image by composing the denoised segments. Various experimental results on synthetic and real noisy images show that our algorithm outperforms state-of-the-art denoising algorithms in removing real signal dependent noise.