LAUSR

To overcome the disadvantages of the traditional block-matching-based image denoising method, an image denoising method based on block matching with 4D filtering (BM4D) in the 3D shearlet transform domain and a generative adversarial network is proposed. Firstly, the contaminated images are decomposed to get the shearlet coefficients; then, an improved 3D block-matching algorithm is proposed in the hard threshold and wiener filtering stage to get the latent clean images; the final clean images can be obtained by training the latent clean images via a generative adversarial network (GAN).Taking the peak signal-to-noise ratio (PSNR), structural similarity (SSIM for short) of image, and edge-preserving index (EPI for short) as the evaluation criteria, experimental results demonstrate that the proposed method can not only effectively remove image noise in high noisy environment, but also effectively improve the visual effect of the images.