Noise removal in the standard RGB (sRGB) space remains a challenging task, in that the noise statistics of real-world images can be different in R, G and B channels. In fact, the green channel usually has twice the sampling rate in raw data and a higher signal-to-noise ratio than red/blue ones. However, the green channel prior (GCP) is often understated or ignored in color image denoising since many existing approaches mainly focus on modeling the relationship among image patches. In this paper, we propose a simple and effective one step GCP-based image denoising (GCP-ID) method, which aims to exploit the GCP for denoising in the sRGB space by integrating it into the classic nonlocal transform domain denoising framework. Briefly, we first take advantage of the green channel to guide the search of similar patches, which improves the patch search quality and encourages sparsity in the transform domain. Then we reformulate RGB patches into RGGB arrays to explicitly characterize the density of green samples. The block circulant representation is utilized to capture the cross-channel correlation and the channel redundancy. Experiments on both synthetic and real-world datasets demonstrate the competitive performance of the proposed GCP-ID method for the color image and video denoising tasks. The code is available at github.com/ZhaomingKong/GCP-ID.

翻译：在标准RGB（sRGB）空间中进行噪声去除仍然是一项具有挑战性的任务，因为真实世界中图像的噪声统计特性在R、G、B三个通道上可能存在差异。事实上，绿色通道在原始数据中的采样率通常是红色/蓝色通道的两倍，且信噪比更高。然而，在彩色图像去噪中，绿色通道先验（GCP）往往被低估或忽视，因为许多现有方法主要关注于建模图像块之间的关系。本文提出了一种简单而有效的基于绿色通道先验的一步式图像去噪方法（GCP-ID），旨在通过将绿色通道先验集成到经典的非局部变换域去噪框架中，在sRGB空间中利用该先验进行去噪。简而言之，我们首先利用绿色通道引导相似块搜索，从而提高块搜索质量并促进变换域中的稀疏性。然后，我们将RGB块重新构造为RGGB数组，以显式表征绿色样本的密度。采用块循环矩阵表示来捕捉跨通道相关性和通道冗余性。在合成数据集和真实数据集上的实验表明，所提出的GCP-ID方法在彩色图像和视频去噪任务中具有竞争力的性能。代码可在github.com/ZhaomingKong/GCP-ID获取。