Noise level estimation using locality preserving natural image statistics

Natural images are known to have certain regular statistical properties. These properties get modified under any artificial change or distortion in natural images. Most common form of image degradation occurs in the form of noise. The amount of degradation in noisy images is measured by estimating the noise level. Many image processing applications such as denoising, restoration, segmentation, compression etc. use noise level information as a prior; inaccurate estimate of which may impact their performance. In this article, we explore natural image statistics in locality preserving transform domain. This property groups structurally similar images/image patches when projected in the transform domain. Image patches corrupted with similar noise level get projected close by in the locality preserving domain and show consistent coefficient behaviour. In particular, we use Two Dimensional Orthogonal Locality Preserving Projection (2DOLPP) as the domain transformation technique. 2DOLPP basis, representing natural images, are learnt in advance from a set of clean images, thereby reducing the computational time significantly. Features based on natural image statistics are extracted from 2DOLPP domain representation of input image patches. Mapping from feature space to noise level is carried out using support vector regression. The proposed noise estimation approach is at par with or surpasses the state-of-the-art techniques with much less computational time. Performance of this approach is stable across a wide range of noise levels and independent of the image structure.