Image restoration is the process of restoring an image from a degraded version, which is usually blurred and noisy. We are motivated by the problem of restoring blurred and noisy images using multiscale weighted Schatten p-norm minimization, which not only gives a better approximation to the original low-rank hypothesis but also considers the importance of different rank components. Similar patches are vectorized and grouped to construct a noisy low-rank matrix. Weighted Schatten p-norm minimization values of all image patch groups are simultaneously penalized by a new regularization term, which can represent both the sparsity and self-similarity of the image structure accurately. In addition, by calculating the similarity of patches on different scales of the image, the restoration effect of the image is further improved. The experimental results show that our method is superior to some existing excellent algorithms in both numerical and visual effects.