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 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 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 noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real noisy images.

提出了一种基于外部先验和内部先验学习的方法来优化实际噪声图像去噪问题，通过学习来自干净图片的外部先验和自适应的内部先验来构建正交字典以恢复原始图像。该方法在多个实际噪声图像数据集上进行了广泛的实验，并表现出高度竞争性的去噪性能，优于特定于实际噪声图像的最先进去噪方法。

基于外部先验指导的内部先验学习用于实际噪声图像去噪