Rethink Gaussian Denoising Prior for Real-World Image Denoising

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.