  Though achieving excellent performance in some cases, current unsupervised
learning methods for single image denoising usually have constraints in
applications. In this paper, we propose a new approach which is more general
and applicable to complicated noise models. Utilizing the property of score
function, the gradient of logarithmic probability, we define a solving system
for denoising. Once the score function of noisy images has been estimated, the
denoised result can be obtained through the solving system. Our approach can be
applied to multiple noise models, such as the mixture of multiplicative and
additive noise combined with structured correlation. Experimental results show
that our method is comparable when the noise model is simple, and has good
performance in complicated cases where other methods are not applicable or
perform poorly.
