Image Inpainting by Multiscale Spline Interpolation
This is an incremental improvement for image restoration tasks, specifically targeting damaged images with scratches and creases.
The paper tackled image inpainting by proposing a multi-scale method that uses both local and global features to recover missing regions, achieving an average 1.2 dB PSNR improvement over existing approaches.
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.