针对一幅模糊图像和一幅噪声图像的图像修复问题，提出了一种结合TV-L1模型与TV模型的新交替修复算法。该算法首先利用TV-L1模型对噪声图像进行去噪；然后利用TV模型，把去噪结果作为迭代初始值，对于模糊图像去模糊；最后，把去模糊结果作为迭代初始值，再利用TV-L1模型对噪声图像进行去噪……，如此交替进行。实验效果表明，该新算法不仅继承了TV-L1模型与TV模型能保持轮廓和细节的优点，同时也有效地克服了这两种模型会降低对比度和出现“重影”的缺点。

In this paper，the image restoration problem is studied with a given blurred image and a noisy image.We propose a new algorithm combining a TV-L1 de-noising model with a total variational deblurring model.In our algorithm，we use TV-L1 model to de-noise the noisy image in the first； Then we use the TV model to deblur the blurred image by taking the de-noised result obtained in the secod step as the initial value of the next iteration； Finally，they use TV-L1 model again to de-noise the noisy image by taking the de-blurred result got in the second step as the initial value of the next iteration...，and so on.Experimental results tell us that our new algorithm not only inherits the advantage of edge preserving of the TV-L1 model and the TV model，but also overcomes its’ disadvantage of degrading the contrast of image.