基于张量总变分的模糊图像盲复原

现有模糊图像盲复原算法通常仅利用彩色图像的灰度信息估计模糊核，彩色图像转换成灰度图像的操作会造成信息丢失，在处理尺寸过小或显著边缘过少的图像时，模糊核的估计通常会失效，导致最后复原图像的质量不理想。针对上述问题，在新的张量框架下，把彩色模糊图像作为一个三阶张量，提出了一种基于张量总变分的模糊图像盲复原算法。首先通过调整张量总变分模型中的正则化参数获取彩色图像不同尺度的边缘信息，从而估计出模糊核；再利用张量总变分算法对模糊图像解模糊，复原出清晰图像。实验结果表明，所提算法得到的复原图像在峰值信噪比（PSNR）和主观视觉上均得到明显改善。

Blind restoration of blurred images based on tensorial total variation

In general blind restoration algorithms, only the gray information of a color image is utilized to estimate the blurring kernel, and thus a restored image may be unsatisfactory if its size is too small or the salient edge in it is too little. Focused on the above mentioned problem, a new blind image restoration algorithm was proposed under a new tensorial framework, in which a color image was regarded as a third-order tensor. First, the blurring kernel was estimated utilizing the multi-scale edge information of blurred color image which could be obtained by adjusting the regularization parameter in tensorial total variation model. Then a deblurring algorithm based on tensorial total variation was adopted to recover the latent image. The experimental results show that the proposed algorithm can achieve obvious improvement on Peak Signal-to-Noise Ratio (PSNR) and subjective vision.