  A wide variety of image denoising methods are available now. However, the
performance of a denoising algorithm often depends on individual input noisy
images as well as its parameter setting. In this paper, we present a
no-reference image denoising quality assessment method that can be used to
select for an input noisy image the right denoising algorithm with the optimal
parameter setting. This is a challenging task as no ground truth is available.
This paper presents a data-driven approach to learn to predict image denoising
quality. Our method is based on the observation that while individual existing
quality metrics and denoising models alone cannot robustly rank denoising
results, they often complement each other. We accordingly design denoising
quality features based on these existing metrics and models and then use Random
Forests Regression to aggregate them into a more powerful unified metric. Our
experiments on images with various types and levels of noise show that our
no-reference denoising quality assessment method significantly outperforms the
state-of-the-art quality metrics. This paper also provides a method that
leverages our quality assessment method to automatically tune the parameter
settings of a denoising algorithm for an input noisy image to produce an
optimal denoising result.
