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

arXiv: https://arxiv.org/abs/1705.04505 · HTML: https://mlsanity.com/paper/1705.04505/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2017.

## Built on by (most cited first, 12 of 12)

- [A Comparison of Image Denoising Methods](https://mlsanity.com/paper/2304.08990/) (2023)
- [Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising](https://mlsanity.com/paper/2209.08094/) (2022)
- [Image Denoising Using Green Channel Prior](https://mlsanity.com/paper/2408.05923/) (2024)
- [Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer](https://mlsanity.com/paper/2407.09094/) (2024)
- [Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair Training](https://mlsanity.com/paper/2304.01805/) (2023)
- [Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography](https://mlsanity.com/paper/2304.10839/) (2023)
- [Optimize the Unseen -- Fast NeRF Cleanup with Free Space Prior](https://mlsanity.com/paper/2412.12772/) (2024)
- [Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool](https://mlsanity.com/paper/2103.16115/) (2021)
- [Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging](https://mlsanity.com/paper/2104.02873/) (2021)
- [Random Weights Networks Work as Loss Prior Constraint for Image Restoration](https://mlsanity.com/paper/2303.16438/) (2023)
- [A New Image Quality Database for Multiple Industrial Processes](https://mlsanity.com/paper/2401.13956/) (2024)
- [Image Denoising Using Global and Local Circulant Representation](https://mlsanity.com/paper/2512.23569/) (2025)

## Abstract (arXiv)

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.