Image denoising methods must effectively model, implicitly or explicitly, the
vast diversity of patterns and textures that occur in natural images. This is
challenging, even for modern methods that leverage deep neural networks trained
to regress to clean images from noisy inputs. One recourse is to rely on
"internal" image statistics, by searching for similar patterns within the input
image itself. In this work, we propose a new method for natural image denoising
that trains a deep neural network to determine whether patches in a noisy image
input share common underlying patterns. Given a pair of noisy patches, our
network predicts whether different sub-band coefficients of the original
noise-free patches are similar. The denoising algorithm then aggregates matched
coefficients to obtain an initial estimate of the clean image. Finally, this
estimate is provided as input, along with the original noisy image, to a
standard regression-based denoising network. Experiments show that our method
achieves state-of-the-art color image denoising performance, including with a
blind version that trains a common model for a range of noise levels, and does
not require knowledge of level of noise in an input image. Our approach also
has a distinct advantage when training with limited amounts of training data.

本论文提出了一种使用深度神经网络和内部图像统计来处理图像去噪的新方法，该方法在子波系数匹配方面表现出色，其性能达到了最先进水平，特别是对于具有强烈噪声干扰的图像，其效果更加明显，而且可以使用有限的训练数据进行训练。