Adaptive Least Squares Acquisition of High Resolution Images

This paper presents a least squares block by block adaptive approach for the acquisition of high resolution (HR) images from available (LR) images. The suggested algorithm is based on the segmentation of the image to overlapping blocks and the interpolation of each block separately. The purpose of the overlapping of blocks is to avoid edge effects. An adaptive 2D least squares approach, which considers the image acquisition model, is used in the minimization of the estimation error of each block. In this suggested algorithm, a weight matrix of moderate dimensions is estimated in a small number of iterations to interpolate each block. This algorithm avoids the large computational complexity due to the matrices of large dimensions required to interpolate the image as a whole. The performance of the proposed algorithm is studied for different LR images with different SNRs. The performance of the proposed algorithm is also compared to the standard as well as the warped distance cubic O-MOMS image interpolation algorithms from the PSNR point of view.

[1]  M. Unser,et al.  Interpolation revisited [medical images application] , 2000, IEEE Transactions on Medical Imaging.

[2]  Giovanni Ramponi,et al.  Warped distance for space-variant linear image interpolation , 1999, IEEE Trans. Image Process..

[3]  Akram Aldroubi,et al.  B-spline signal processing. II. Efficiency design and applications , 1993, IEEE Trans. Signal Process..

[4]  Michael Unser,et al.  B-spline signal processing. I. Theory , 1993, IEEE Trans. Signal Process..

[5]  Michael Unser,et al.  Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

[6]  M. Unser,et al.  Interpolation Revisited , 2000, IEEE Trans. Medical Imaging.

[7]  Tao Chen,et al.  Image interpolation using across-scale pixel correlation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[8]  J. Paik,et al.  Regularized Interative Image Interpolation and its application to Spatially Scalable Coding , 1998, International 1998 Conference on Consumer Electronics.

[9]  M. Hadhoud,et al.  A new edge preserving pixel-by-pixel (PBP) cubic image interpolation approach , 2004, Proceedings of the Twenty-First National Radio Science Conference, 2004. NRSC 2004..

[10]  Jong-Ki Han,et al.  Modified cubic convolution scaler with minimum loss of information , 2001 .

[11]  Philip J. Bones,et al.  Statistical interpolation of sampled images , 2001 .

[12]  H. C. Andrews,et al.  Cubic Splines for image Interpolation and Filtering , 1978 .

[13]  Said Esmail El-Khamy,et al.  Efficient implementation of image interpolation as an inverse problem , 2005, Digit. Signal Process..

[14]  Moawad I. Dessouky,et al.  Regularized super-resolution reconstruction of images using wavelet fusion , 2005 .

[15]  Akram Aldroubi,et al.  B-SPLINE SIGNAL PROCESSING: PART II-EFFICIENT DESIGN AND APPLICATIONS , 1993 .

[16]  Akram Aldroubi,et al.  B-SPLINE SIGNAL PROCESSING: PART I-THEORY , 1993 .

[17]  Thierry Blu,et al.  MOMS: maximal-order interpolation of minimal support , 2001, IEEE Trans. Image Process..

[18]  M. Hadhoud,et al.  Sectioned implementation of regularized image interpolation , 2003, 2003 46th Midwest Symposium on Circuits and Systems.

[19]  M. Hadhoud,et al.  Adaptive image interpolation based on local activity levels , 2003, Proceedings of the Twentieth National Radio Science Conference (NRSC'2003) (IEEE Cat. No.03EX665).

[20]  S.E. El-Khamy,et al.  Optimization of image interpolation as an inverse problem using the LMMSE algorithm , 2004, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521).