结合Tetrolet与主动随机场模型的高斯噪声抑制

Gaussian Noise Reduction with Tetrolet and Active Random Field Model

文中提出一种结合Tetrolet变换和主动随机场模型的去噪方法,用于抑制图像中的高斯噪声.对含有高斯噪声的图像进行Haar小波分解,在小波变换域利用主动随机场算法针对高斯噪声进行去噪,并利用小波逆变换重构去噪后的图像,最后用Tetrolet变换在变换域进一步抑制噪声.实验结果表明,与直接利用小波、Tetrolet、马尔科夫随机场模型以及主动随机场模型等方法相比,该方法对添加不同程度高斯噪声的图像有更好的去噪效果.

In this study, an image denoising algorithm is proposed by combining Tetrolet transform and active random field （ARF）. An image with Caussian noise is decomposed with Haar wavelets, and the ARF algorithm is used to reduce Caussian noise in the wavelet domain. After inverse wavelet transform, the Tetrolet transform is used for further denoising. The proposed method is compared with other denoising algorithms including wavelet algorithm, Tetrolet algorithm, Markov random field based algorithm and ARF based algorithm. Experimental results indicate that the proposed approach can effectively reduce Gaussian noise at various levels, and achieve better results than other algorithms.