There is a strong need to automatically remove noise and degradations from noisy image sequences. Applications areas include Image surveillance, Forensic Image Processing, Digital video broadcasting, Digital Film Restoration, Virtual Studio, Medical Image Processing, Remote Sensing $\ldots$. Image sequence restoration is tightly coupled to motion segmentation. It requires to extract moving objects in order to separately restore the background and each moving region along its particular motion trajectory. Most of the work done to date mainly involves motion compensated temporal filtering techniques with appropriate 2D or 3D Wiener filter for noise suppression, 2D/3D median filtering or more appropriate morphological operators for removing impulsive noise. Usually, motion segmentation and image restoration are tackled separately in image sequence restoration. In this article, the motion segmentation and the image restoration parts are done in a coupled way, allowing the motion segmentation part to positively influence the restoration part and vice-versa. This is the key of our approach that allows to deal simultaneously with the problem of restoration and motion segmentation. To take into account both requirements, we present an original PDE based method which permits to solve the two problems in a coupled way. This PDE based approach allows to anisotropically restore the image sequence : edges are well preserved and blur is not introduced during the restoration process. To this end, we reformulate the image sequence restoration problem as an energy functional minimization. A suitable numerical scheme based on half-quadratic minimization is proposed and its stability demonstrated. Experimental results obtained on noisy synthetic data and real images will illustrate the capabilities of this original and efficient approach.