Rethink Gaussian Denoising Prior for Real-World Image Denoising
Document Type
Conference Proceeding
Publication Date
11-4-2019
School
Computing Sciences and Computer Engineering
Abstract
Real-world image denoising is a challenging but significant problem in computer vision. Unlike Gaussian denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian denoising approach on real-world denoising problems. In this paper, we propose a simple framework for effective real-world image denoising. Specifically, we investigate the intrinsic properties of the Gaussian denoising prior and demonstrate this prior can aid real-world image denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian denoising prior can be also transferred to real-world image denoising by exploiting appropriate training schemes.
Publication Title
2019 IEEE 31st International Conference On Tools With Artificial Intelligence
Recommended Citation
Yang, T.,
Huan, J.,
Li, B.,
Hu, K.
(2019). Rethink Gaussian Denoising Prior for Real-World Image Denoising. 2019 IEEE 31st International Conference On Tools With Artificial Intelligence.
Available at: https://aquila.usm.edu/fac_pubs/18004