Color Image Denoising Using The Green Channel Prior
Zhaoming Kong Xiaowei Yang
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.