Digital Image Inpainting Using Cellular Neural Network and Contour Tracking Using Run Length Coding

Abstract: Digital Image Inpainting is challenging and interesting research area, because one has to restore the area which is not visible but important to visually complete the image. This technique has found widespread use in applications such as restoration, error recovery, multimedia editing, and video privacy protection. Because of the strong human visual perception, a very effective technique is required for digital image inpainting. Most automatic techniques are computationally intensive and unable to repair large holes. Existing methods use interpolation methods where surrounding information is not adequate for image interpolation and chain codes for contour matching for small damaged area reconstruction. But, reconstructed image has not given up to the mark results. This paper proposed an effective inpainting technique in order to improve the inpainting result. The method proposed in this paper uses Run Length Coding for shape tracking along with CNN approach, because Run Length coding track the shape of an image, which is better than the several methods available for shape tracking.

Keywords: Image inpainting, Cellular Neural Network, Digital images, contour matching, Run Length Code