The week's pick
Random Articles
Reseach Article
Image Restoration using 3-Dimensional Discrete Cosine Transform
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 156 - Number 9 |
| Year of Publication: 2016 |
| Authors: D. Ramesh Varma, G. Prasanna Kumar, P. S. N. Murthy |
10.5120/ijca2016912545
|
D. Ramesh Varma, G. Prasanna Kumar, P. S. N. Murthy . Image Restoration using 3-Dimensional Discrete Cosine Transform. International Journal of Computer Applications. 156, 9 ( Dec 2016), 16-22. DOI=10.5120/ijca2016912545
Abstract
Image restoration is one of the major issues in the field of image processing. Generally, images are corrupted due to the ageing effect. The previous techniques are based on only single domain i.e.., either local domain or non-local domain. This paper present a new technique called “Image restoration using 3-dimensional discrete cosine transform” which comprises of two domains. Initially, smoothing of image is performed with the help of horizontal and vertical difference operators in the local domain and then synthesizes the textures with the help of discrete cosine transform in non-local domain. A split bregman iterative algorithm is developed to make these two domains more tractable and robust. In this paper, the problem of removing text in an image and filling the corrupted regions is dealt with the help of proposed technique. The proposed method achieves significant performance improvements over the existing state-of-art schemes.
References
- “Image Inpainting-Automatic Detection and Removal of Text From Images” by Uday Modha and Preeti Dave
- “Region filling and Object Removal by Exemplar-Based Image Inpainting” by A. Criminisi, P. P´erez and K. Toyama
- “Parametric generalized gaussian density estimation” by M.K.Varanasi and B.Aazhang.
- “Compression artifact reduction by overlapped-block transform coefficient estimation with block similarity” by X.Zhang, R.Xiong, S.Ma, X.Fan and W.Gao.
- “Point wise shape-adaptive DCT for high quality denoising and deblocking of grayscale and color images” by V.Katkovnik, A.Foi and K.Egiazarian.
- “Texture synthesis by non-parametric sampling” by T.K.Leung and A.Efros.
- “Image restoration by sparse 3D transform-domain collaborative filtering” by K.Dabov, A.Foi and V.katkovnik.
- “Image restoration using Joint statistical modelling in a space-transform domain” by Jian Zhang, Debin Zhao, Wen GAO, Siwei Ma and Ruiqin Xiong.
- “The split Bergman iterative algorithm for L1 regularized problems” by T.Goldstein and S.osher.
- “Split Bregman methods and frame based image restoration,” by J. F. Cai, S. Osher, and Z. W. Shen
- “Non-local discrete regularization on weighted graphs” by A.Elmaotaz, S.Bougleux and O.Lezoray.
- R.Fergus and D.Krishnan “Fast image deconvolution using hyper laplacian priors”.
- “Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems,” by A. Beck and M. Teboulle.
- “A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration” by J.Bioucas-Dias and M.Figueiredo.
- “3D data denoising and inpainting with the fast curvelet transform” by A. Woiselle, J. L. Starck, and M. J. Fadili.
- J. Yang, Y. Wang, H. Wang, K. Hua, W. Wang, and J. Shen. “Automatic objects removal for scene completion”.
- P. P. A. Criminisi and K. Toyama. “Object Removal by Exemplar-Based Inpainting.
- T. Shih and R. Chang, “Digital Inpainting-Survey and Multilayer Image Inpainting Algorithms”.
- K. Jain, and B. Yu, “Automatic Text Location in Images and Video Frames”
- “Simultaneous cartoon and texture image inpainting using morphological component analysis” by M.Elad, L.Starck, P.Querre and D.L.Donoho.
- “Patch-based Texture Synthesis for Image Inpainting” by Tao Zhou, Johnson.
- Image sharpening using un-sharp filters
Index Terms
Keywords