Most image denoising methods only consider the non-local self-similarity（ NSS） of noise input images

and collect similar image blocks only from degraded input images. To some extent

the effects of image denoising depends on the input image. Aiming at the problem of denoising in image restoration

a group sparse denoising model based on convolutional neural network is established. Using two NSS priors（ i. e. NSS priors for noise input images and pre-filtering images）

the image denoising problem is transformed into group sparse residual minimization problem. Besides

to improve the accuracy of non-local similar blocks selecting

an adaptive block search method is used

group sparse coefficient of original image and convolutional neural network is used to prefilter image

in order to estimate well. The experimental results show that the proposed GSRC-CNN method outperforms many state-of-the-art image denoising methods in terms of objective and perceived quality.