Abstract
This paper presents an adaptive block-by-block least squares (LS) algorithm for the interpolation of infrared (IR) images. The suggested algorithm is based on the segmentation of the low resolution (LR) image into overlapping blocks and the interpolation of each block, separately. The purpose of the overlapping is to avoid edge effects between blocks. An iterative implementation of the proposed algorithm, which considers the image acquisition model, is used for the minimization of the estimation error in each block. A weight matrix of moderate dimensions is estimated in a small number of iterations to interpolate each block. This proposed algorithm avoids the large computational complexity resulting from the matrices of large dimensions required to interpolate the image as a whole. The performance of the proposed algorithm is compared with the standard as well as the warped distance optimal interpolation of maximal order with minimal support (O-MOMS) algorithm from the peak signal-to-noise ratio (PSNR) point of view. Numerical results reveal the superiority of the proposed LS algorithm to the cubic O-MOMS algorithm.
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References
T. Blu, P. Thevenaz, M. Unser, MOMS: Maximal-order interpolation of minimal support. IEEE Trans. Image Process. 10(7), 1069–1080 (2001)
T. Chen, H.R. Wu, B. Qiu, Image interpolation using across-scale pixel correlation, in Proceedings of ICASSP (2001)
S.E. El-Khamy, M.M. Hadhoud, M.I. Dessouky, F.E. Abd El-Samie, Adaptive image interpolation based on local activity levels, in Proceedings of the National Radio Science Conference of Egypt, 2003
S.E. El-Khamy, M.M. Hadhoud, M.I. Dessouky, B.M. Salam, F.E. Abd El-Samie, A new edge preserving pixel-by-pixel (PBP) cubic image interpolation approach, in Proceedings of the National Radio Science Conference of Egypt, 2004
S.E. El-Khamy, M.M. Hadhoud, M.I. Dessouky, B.M. Salam, F.E. Abd El-Samie, Optimization of image interpolation as an inverse problem using the LMMSE algorithm, in Proceedings of IEEE MELECON, 2004
S.E. El-Khamy, M.M. Hadhoud, M.I. Dessouky, B.M. Salam, F.E. Abd El-Samie, Sectioned implementation of regularized image interpolation, in Proceedings of 46th IEEE MWSCAS, 2003
J.K. Han, H.M. Kim, Modified cubic convolution scaler with minimum loss of information. Opt. Eng. 40(4), 540–546 (2001)
H.S. Hou, H.C. Andrews, Cubic spline ror image interpolation and digital filtering. IEEE Trans. Acoust. Speech Signal Process. ASSP-26(9), 508–517 (1978)
W.Y. V Leung, P.J. Bones, Statistical interpolation of sampled images. Opt. Eng. 40(4), 547–553 (2001)
G. Ramponi, Warped distance for space variant linear image interpolation. IEEE Trans. Image Process. 8, 629–639 (1999)
J.H. Shin, J.H. Jung, J.K. Paik, Regularized iterative image interpolation and its application to spatially scalable coding. IEEE Trans. Consum. Electron. 44(3), 1042–1047 (1998)
P. Thevenaz, T. Blu, M. Unser, Interpolation revisited. IEEE Trans. Med. Imaging 19(7), 739–758 (2000)
M. Unser, Splines: a perfect fit for signal and image processing. IEEE Signal Processing Magazine (1999)
M. Unser, A. Aldroubi, M. Eden, B-spline signal processing: Part I—Theory. IEEE Trans. Signal Process. 41(2), 821–833 (1993)
M. Unser, A. Aldroubi, M. Eden, B-spline signal processing: Part II—Efficient design and applications. IEEE Trans. Signal Process. 41(2), 834–848 (1993)
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Ashiba, H.I., Awadalla, K.H., El-Halfawy, S.M. et al. Adaptive Least Squares Interpolation of Infrared Images. Circuits Syst Signal Process 30, 543–551 (2011). https://doi.org/10.1007/s00034-010-9243-z
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DOI: https://doi.org/10.1007/s00034-010-9243-z