为改进K-SVD方法抑制强噪声的效果,提出一种小波域稀疏冗余表示图像去噪方法——单尺度低频小波K-SVD(SLWK-SVD)。首先对含噪图像做单尺度小波变换,然后用K-SVD算法对变换后的图像逼近系数学习过完备自适应字典,而对于高频小波系数则简单置零,最后用逆小波变换得到恢复图像。实验结果表明,与K-SVD方法相比,所提方法具有良好的抑制强噪声能力,在所给强噪声下(方差介于50和100),恢复图像信噪比提高了约0.5—1.5 dB,并克服了K-SVD方法去噪后图像出现的明显波动效应,具有更佳的视觉效果。

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.