  Recovering the missing regions of an image is a task that is called image
inpainting. Depending on the shape of missing areas, different methods are
presented in the literature. One of the challenges of this problem is
extracting features that lead to better results. Experimental results show that
both global and local features are useful for this purpose. In this paper, we
propose a multi-scale image inpainting method that utilizes both local and
global features. The first step of this method is to determine how many scales
we need to use, which depends on the width of the lines in the map of the
missing region. Then we apply adaptive image inpainting to the damaged areas of
the image, and the lost pixels are predicted. Each scale is inpainted and the
result is resized to the original size. Then a voting process produces the
final result. The proposed method is tested on damaged images with scratches
and creases. The metric that we use to evaluate our approach is PSNR. On
average, we achieved 1.2 dB improvement over some existing inpainting
approaches.
