基于非降采样轮廓波变换的图像修复算法

多尺度分析技术已经广泛应用于数字图像处理领域，较大破损区域的图像修复成为图像修复的一个热点和难点。针对该问题，结合多分辨率分析原理与传统的样本块图像修复技术，提出了一种基于非降采样轮廓波变换的图像修复算法。该算法利用非降采样轮廓波变换把图像分解成低频部分和高频部分，并对图像分解后不同频率的部分分别予以修复。其中，图像的低频成分采用改进的纹理合成的方法进行修复。因为图像经过非降采样轮廓波变换后，低频分量与高频分量之间对应位置的信息之间具有一致性的特点，所以在修复低频成分的同时实现其他高频分量对应位置信息的修复。最后通过非降采样轮廓波重构过程完成纹理图像的修复。一般图像修复方法的参数选取以图像的修复效果最佳为宜，给出一个反例进行分析论证。实验发现，所提算法所修复图像的结构相似性测度与经典Criminisi算法和小波修复算法相差不大，但是峰值信噪比（PSNR）测度依据不同图像的纹理结构的特点与破损区域的不同位置特点而不同。仿真实验表明，所提方法很好地推广了非降采样轮廓波变换在图像修复中的应用，并且在修复大区域破损图像时能够获得较好的修复效果。

Image inpainting algorithm based on non-subsampled contourlet transform

The multi-scale analysis technology has been widely used in the field of digital image processing, the inpainting image with large damaged area has become a hot and difficult spot of image inpainting. Based on the principle of multi-resolution analysis and the traditional method of image inpainting, a new algorithm for image inpainting based on non-subsampled contourlet transform was proposed. Firstly, the image was decomposed into low frequency and high frequency parts by using the non-subsampled contourlet transform, then the parts of different frequency after image decomposition were inpainted respectively. The low frequency components of the image were inpainted by the improved method of texture synthesis. Because after non-subsampled contourlet transform, the information of the corresponding position between the low frequency component and the high frequency component is consistent, the information of corresponding position of other high frequency components could be repaired while the low frequency component was repaired. Finally, the inpainting of the texture image was completed by reconstruction process of non-subsampled contourlet transform. Generally, the selection of image inpainting parameters was appropriate for the best image effect, thus a counter-example was given for authentication. The structural similarity measure among the proposed algorithm and the classical Criminisi algorithm and the wavelet inpainting algorithm has little difference, but the Peak Signal-To-Noise Ratio (PSNR) measurement has different result according to the different texture characteristics of images and the different location characteristics of damaged areas. The simulation results show that the proposed method is very good for the promotion of the non-subsampled Contourlet transform in image inpainting application, and it can get better repair effect while inpainting the image with large damaged area.