Noise Estimation of Natural Images via Statistical Analysis and Noise Injection

We develop a framework for estimating the noise level of a natural image using two important statistics: 1) high kurtosis and 2) scale invariance in transform domain. By exploring the said priors of natural image statistics in 2-D discrete cosine transform (DCT) domain, we reveal the limitations of these statistics for images with highly directional edges or large smooth areas. Then, we derive a novel two-step estimation scheme for noise variance: 1) in preliminary estimation, an integration of wavelet and nondirectional DCT transform is used to alleviate the influence of image's structures and 2) a noise-injection rectification is further devised to deal with the noise-free image contents. A simulation and comparative study demonstrates that this algorithm reliably infers noise variance and its robustness over wide ranges of visual content and noise levels, while outperforming some relevant methods. This paper can significantly improve the performance of existing denoising techniques that require the noise variance as a critical parameter.