  Filtering real-world color images is challenging due to the complexity of
noise that can not be formulated as a certain distribution. However, the rapid
development of camera lens pos- es greater demands on image denoising in terms
of both efficiency and effectiveness. Currently, the most widely accepted
framework employs the combination of transform domain techniques and nonlocal
similarity characteristics of natural images. Based on this framework, many
competitive methods model the correlation of R, G, B channels with pre-defined
or adaptively learned transforms. In this chapter, a brief review of related
methods and publicly available datasets is presented, moreover, a new dataset
that includes more natural outdoor scenes is introduced. Extensive experiments
are performed and discussion on visual effect enhancement is included.
