In order to improve the noise handling of-SVD strong method

we propose a new image denoising method based on a sparse and redundant representations model in the wavelet domain called Single Scale Low-frequency Wavelet K-SVD (SLWK-SVD). The basic idea is to follow three steps: first

use the wavelet transform on the noisy image

then employ the K-SVD algorithm on the low-frequency wavelet coefficients

and finally

replac the high-frequency wavelet coefficients by zeros. The experimental results show that compared to the K-SVD method

the proposed method is more robust to strong noise. At the given strong noise level (variance from 50 to 100)

the PSNR of the denoised image improved about 0.5—1.5 dB. Meanwhile

the proposed method can overcome the problem of fluctuation of the denoised image when using the K-SVD

and improve the visual effect of the recoverde image.