# External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising

## TL;DR

This paper introduces a novel denoising approach that combines external clean image priors with internal image priors learned from the noisy image itself, effectively handling complex real-world noise.

## Contribution

It proposes an external prior guided internal prior learning framework that adaptively combines external and internal priors for improved real-world noisy image denoising.

## Key findings

- Outperforms state-of-the-art denoising methods on real-world datasets.
- Effectively models complex real-world noise beyond simple distributions.
- Demonstrates the benefit of combining external and internal priors.

## Abstract

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.