A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution)

Superresolution produces high-quality, high-resolution images from a set of degraded, low-resolution images where relative frame-to-frame motions provide different looks at the scene. Superresolution translates data temporal bandwith into enhanced spatial resolution. If considered together on a reference grid, given low-resolution data are nonuniformly sampled. However, data from each frame are sampled regularly on a rectangular grid. This special type of nonuniform sampling is called interlaced sampling. We propose a new wavelet-based interpolation-restoration algorithm for superresolution. Our efficient wavelet interpolation technique takes advantage of the regularity and structure inherent in interlaced data, thereby significantly reducing the computational burden. We present one- and two-dimensional superresolution experiments to demonstrate the effectiveness of our algorithm.

[1]  D. M. Etter,et al.  Wavelet basis reconstruction of nonuniformly sampled data , 1998 .

[2]  Daniel Gross,et al.  Improved resolution from subpixel shifted pictures , 1992, CVGIP Graph. Model. Image Process..

[3]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

[4]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[5]  O. Axelsson.  Iterative solution methods , 1995 .

[6]  W. Lukosz.  Optical Systems with Resolving Powers Exceeding the Classical Limit , 1966 .

[7]  A. Murat Tekalp,et al.  High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Eero P. Simoncelli.  Statistical models for images: compression, restoration and synthesis , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[9]  Gene H. Golub,et al.  Blind Superresolution with Generalized Cross-Validation using Gauss-Type Quadrature Rules , 1999 .

[10]  Jan P. Allebach,et al.  Iterative reconstruction of bandlimited images from nonuniformly spaced samples , 1987 .

[11]  Y. Meyer.  Principe d'incertitude, bases hilbertiennes et algèbres d'opérateurs , 1986 .

[12]  Robert D. Nowak,et al.  Wavelet-based statistical signal processing using hidden Markov models , 1998, IEEE Trans. Signal Process..

[14]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Rama Chellappa,et al.  Data-driven multichannel superresolution with application to video sequences , 1999 .

[16]  G. Golub,et al.  Blind restoration/superresolution with generalized cross-validation using Gauss-type quadrature rules , 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

[17]  Kiyoharu Aizawa,et al.  Acquisition of very high resolution images using stereo cameras , 1991, Other Conferences.