  Most of existing image denoising methods learn image priors from either
external data or the noisy image itself to remove noise. However, priors
learned from external data may not be adaptive to the image to be denoised,
while priors learned from the given noisy image may not be accurate due to the
interference of corrupted noise. Meanwhile, the noise in real-world noisy
images is very complex, which is hard to be described by simple distributions
such as Gaussian distribution, making real-world noisy image denoising a very
challenging problem. We propose to exploit the information in both external
data and the given noisy image, and develop an external prior guided internal
prior learning method for real-world noisy image denoising. We first learn
external priors from an independent set of clean natural images. With the aid
of learned external priors, we then learn internal priors from the given noisy
image to refine the prior model. The external and internal priors are
formulated as a set of orthogonal dictionaries to efficiently reconstruct the
desired image. Extensive experiments are performed on several real-world noisy
image datasets. The proposed method demonstrates highly competitive denoising
performance, outperforming state-of-the-art denoising methods including those
designed for real-world noisy images.
