Adaptive, optimal-recovery image interpolation

We consider the problem of image interpolation using adaptive optimal recovery. We adaptively estimate the local quadratic signal class of our image pixels. We then use optimal recovery to estimate the missing local samples based on this quadratic signal class. This approach tends to preserve edges, interpolating along edges and not across them.

[1]  Charles A. Micchelli,et al.  A Survey of Optimal Recovery , 1977 .

[2]  Ron Kimmel,et al.  Demosaicing: Image Reconstruction from Color CCD Samples , 1998, ECCV.

[3]  Martin Vetterli,et al.  Resolution enhancement of images using wavelet transform extrema extrapolation , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[4]  Michael Golomb,et al.  OPTIMAL APPROXIMATIONS AND ERROR BOUNDS , 1958 .

[5]  Martin Vetterli,et al.  Resolution enhancement of images using wavelet extrema extrapolation , 1995 .

[6]  Thomas W. Parks,et al.  Prediction of image detail , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[7]  Ping Wah Wong,et al.  Edge-directed interpolation , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[8]  Sheila S. Hemami,et al.  Regularity-preserving image interpolation , 1999, IEEE Trans. Image Process..