Computer Science & Electrical
Received: 08 Sep 2018 , Published: 08 September 2018
Views: 35 , Download: 22
|1||Ah Nge Htwe|
Image reconstruction is an important task in many applications of computer vision. The objective of image reconstruction is to recover a degraded image based on mathematical and statistical models. When an image is sent via network, image’s size is reduced to fast and low cost. Although the small size images are very useful in the data storage and image transmission, the detail and clear information can’t be received from small image. So, small image is enlarged to get visual contents clearly. In enlarged image, some pixel positions are needed to fill Interpolation is the process which generate pixels to fill in the blanks. In this paper, image reconstruction approach is developed based on bicubic interpolation method. This method can produce good image with fine detailed information. This approach can be used to assist in photo editing processes and other computer vision applications.
- T. Wittman. “Mathematical Techniques for Image Interpolation”, Department of Mathematics, University of Minnesota, 2005
- V. H. Patil and D. S. Bormane, “Wavelet for Medical Image Enhancement to Assist Resizing”, GVIP Journal, Vol. 5, Issue 9, Dec 2005.
- A. Wong, P. Fi and D. Clausi, “A Perceptually Adaptive Approach to Image Denoising using Anisotropic Non-local Means”, In proceedings of IEEE International Conference on Image Processing (ICIP), 2008.
- B. S. Morse and D. Schwartzwald, “Image Magnification Using Level-Set Reconstruction”, 2002.
- M. Ebrahimi and E. R. Vrscay, “Nonlocal-means single-frame image zooming”, Proceedings in Applied Mathematics and Mechanics, Oct, 2007.
- T. Acharya and P. S. Tsai, “Computational Foundations of Image Interpolation Algorithms, ACM Ubiquity, Vol. 8, 2007.
- T. M. Lehmann, C. Gonner, and K. Spitzer, “ Survey: Interpolation Methods in Medical Image Processing”, IEEE Transactions on Medical Imaging, Vol. 18, No. 11, November 1999.
- H. Q. Luong, A. Ledda and W. Philips, “Non-Local Image Interpolation”, IEEE proceedings, 2006.
- A. Wong, P. Fieguth, D. Clausi, “A Perceptually Adaptive Approach to Image Denoising Using Anisotropic Non-Local Means”, IEEE Proceedings, 2008.
- C. Kervrann, J. Boulanger and P. Coupe, “Bayesian Non-Local Means Filter, Image Redundancy and Adaptive Dictionaries for Noise Removal”, Proceedings, 2006.
- J. V. Manjon, M. Robles and N. A. Thacker, “Multispectral MRI De-noising Using Non-Local Means”, In Proceedings MIUA, 2007.
- J. Orchard, A. Wong, “Faster Nonlocal-Means Image Denoising using the FFT”, IEEE Transaction, Image, Proceedings, 2007.