Nonlocal means (NLM) is a successful method to denoise digital images. However

the decay control parameter is usually fixed and cannot be adaptively changed according to the image content adaptively. Some intensified researches on the influence of the decay control parameter that describes the width of the smoothing kernel and data-dependence have been done to make the NLM more effective. Therefore

this paper proposes to apply the no-reference image content metric (denoted as Q) which does not need the noise to be Gaussian to NLM to optimize the fixed decay control parameter without a reference image. Some methods have been proposed to use SURE (Stein's unbiased risk estimate) as an estimator of mean squared error (MSE) of NLM to select the optimal parameters for restoring an image. However

these methods are fit only for Gaussian noise. Moreover

in real situations

the noise of an image is usually not Gaussian

and the variance of the noise is also space varying. Thus

in this situation

selecting the optimal parameters via SURE will be ineffective. Therefore

this paper first proposes to incorporate the no-reference image content metric Q which does not need the noise to be Gaussian to NLM to optimize the fixed decay control parameter in an iterative way and obtain the optimal denoised results. Second

aiming at the linear relationship between the Rician noise level in magnetic resonance imaging (MRI) images and the optimal decay control parameter

we propose to tune the parameter of unbiased NLM for MRI denoising according to the structure information (such as texture and edge) of the image. This method measures textures and edges in an image via the metric Q. By considering the structure information of the image

we can tune down the decay control parameter when the value of Q is high so that the textures and edges can be retained in the denoised results. Experimental results show that our proposed method can improve the value of peak signal-to-noise ratio. At the same time

the denoised results obtained by the proposed method are superior to the results obtained by the traditional methods in vision. Incorporating the image content metric to NLM is valid for denoising. We propose to optimize the parameter of NLM via the no-reference image content metric Q. The proposed method aims to enable the NLM method to choose the decay control parameter adaptively according to the image content. Experimental results show that the proposed method can optimize the NLM in terms of vision and PSNR. Thus

incorporating the image content metric to NLM for denoising is valid.