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.%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0.5-1.5dB,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.
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