Abstract—Digital Image restoration methods provide means for rebuilding of small damaged portions of an image. Image or video resources are often received in poor conditions, mostly with noise or defects making the resources difficult to read and understand. Some methods are presented that can be used for the reconstruction of damaged or partially known images. These methods will use interpolation methods where surrounding information is not adequate. Further, Reconstructed image will not give better results. We propose an effective algorithm with CNN and Contour matching, that can be used to inpainting digital images or video frames with very high noise ratio. Noises inside the cell with different sizes are inpainted in spite of proximity cells not supporting to reconstruction. So, the result showed that an almost blurred image or unrecognized cell can be recovered with visually good effect. The proposed technique takes the possibility of direct implementation of an existing CNN chip into account, in a single step, by using 3x3 dimensional linear reaction templates. This same method can be further used for processing motion picture with high percentage of noise.
Index Terms—Image inpainting, Cellular Neural Network, Digital images, PSNR ratio, contour matching
aDepartment of Computer Applications, National Institute of Technology, Tiruchirappalli – 620 015, INDIA
bDepartment of Mathematics, National Institute of Technology, Tiruchirappalli – 620 015, INDIA (e-mail: email@example.com).
Cite: P. Elango and K. Murugesan, "Image Restoration Using Cellular Neural Network With Contour Tracking Ideas," International Journal of Computer Theory and Engineering vol. 2, no. 5, pp. 724-729, 2010.