Image Reconstruction by Prioritized Incremental Normalized Convolution
A priority-based method for pixel reconstruction and incremental hole filling in incomplete images and 3D surface data is presented. The method is primarily intended for reconstruction of occluded areas in 3D surfaces and makes use of a novel prioritizing scheme, based on a pixelwise defined confidence measure, that determines the order in which pixels are iteratively reconstructed. The actual reconstruction of individual pixels is performed by interpolation using normalized convolution.
The presented approach has been applied to the problem of reconstructing 3D surface data of a rock pile as well as randomly sampled image data. It is concluded that the method is not optimal in the latter case, but the results show an improvement to ordinary normalized convolution when applied to the rock data and are in this case comparable to those obtained from normalized convolution using adaptive neighborhood sizes.
Keywordsimage reconstruction hole filling normalized convolution
- 1.Averbuch, A., Gelles, G., Schclar, A.: Fast hole-filling in images via fast comparison of incomplete patches. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 738–744. Springer, Heidelberg (2006)Google Scholar
- 2.Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2000, pp. 417–424. ACM Press, New York (2000)Google Scholar
- 4.Farnebäck, G.: Polynomial Expansion for Orientation and Motion Estimation. PhD thesis, Linköping University, Sweden (2002)Google Scholar
- 6.Knutsson, H., Westin, C.-F.: Normalized and differential convolution. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1993, pp. 515–523 (June 1993)Google Scholar