Observed images can be easily contaminated by various noises during acquisition or transmission. As an important preprocessing module for various image processing systems

image denoising has been explored extensively in the last few decades. Various image denoising algorithms have been developed to improve the quality of images corrupted by some form of noise model

which is frequently assumed to be additive white Gaussian noise in the literature. As the core parameter of the non-blind block-matching and 3D filtering (BM3D) algorithm

noise level (i.e.

variance) should be set manually in actual applications. This procedure significantly affects the noise reduction performance of the BM3D algorithm and limits its application scope due to inaccurate noise level estimation. To resolve this problem

a novel local means estimation (LME) algorithm that is utilized as the preprocessing module of the BM3D algorithm is proposed. In this work

we focus on solving the problem based on the quality-aware feature extraction of natural scene statistics (NSS) and local means techniques

which we can apply to automatically predict the noise level parameter with high accuracy and efficiency. Research on NSS has clearly demonstrated that clean images of natural scenes belong to a small set of space of all possible images and exhibit strong predictable statistical regularities in the spatial or frequency domain that can distinguish them from corrupted ones. By contrast

nonlocal means-based estimation exhibits the right features that interest us

such as its conceptual simplicity and effectiveness. In particular

several widely representative and clean images were selected and corrupted by Gaussian noise with different variances to constitute a set of distorted images. The sub-band coefficients of a corrupted image obtained from wavelet transform over three scales and three orientations were parameterized using generalized Gaussian distribution. These estimated parameters were used to form a feature vector that described image noise level. The feature vector extracted from each distorted image belonging to the distorted image set and the corresponding Gaussian noise variance constituted the feature vector database. The proposed quality-aware features have extremely low computational complexity

thereby making them appropriate for time-constrained applications. During the noise reduction stage

the feature vector of a noisy image to be denoised was extracted using the same feature extraction approach. We selected the feature vectors and the corresponding noise level values that were similar to the extracted feature of the noisy image in the feature vector database to estimate its variance using the LME approach. The estimated variance was subsequently used as the input parameter of the BM3D algorithm. The BM3D algorithm was transformed into a blind denoising algorithm called BM3D based on the LME algorithm (BM3D-LME). The accurate estimation of noise level from a single noisy image is of fundamental interest in a wide variety of digital image processing applications. This procedure is highly important for tasks such as denoising

super-resolution

and segmentation. We verified the accuracy

robustness

and effectiveness of the proposed method on a large number of representative images from several benchmark databases. Experimental results show that the LME algorithm can accurately and rapidly estimate the noise level in any image to be denoised. The actual noise reduction effect of the BM3D algorithm is effectively improved with the aid of LME

and its application scope is also expanded.