Abstract
A novel super-resolution approach is presented. It is based on the local Lipschitz regularity of wavelet transform along scales to predict the new detailed coefficients and their gradients from the horizontal, vertical and diagonal directions after extrapolation. They form inputs of a synthesis wavelet filter to perform the undecimated inverse wavelet transform without registration error, to obtain the output image and its gradient map respectively. Finally, the gradient descent algorithm is applied to the output image combined with the newly generated gradient map. Experiments show that our method improves in both the objective evaluation of peak signal-to-noise ratio (PSNR) with the greatest improvement of 1.32 dB and the average of 0.56 dB, and the subjective evaluation in the edge pixels and even in the texture regions, compared to the “bicubic” interpolation algorithm.
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References
VJiji, C., Chaudhuri, S., Chatterjee, P.: Single frame image super-resolution: should we process locally or globally? Multidimensional Systems and Signal Processing 18, 123–152 (2007)
Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. on PAMI 24(9), 1167–1183 (2002)
Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neigbor embedding. In: Proc. of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 275–282 (2004)
Chang, S.G., Cvetkovic, Z., Vetterli, M.: Resolution enhancement of images using wavelet transform extrema extrapolation. In: Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, pp. 2379–2382 (1995)
Mallat, S., Hwang, W.L.: Singularity detection and processing with wavelets. IEEE Trans. Information Theory 38(2), 617–643 (1992)
Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multi resolution image representation. IEEE Trans. on Image Processing 14(12), 2091–2106 (2005)
Velisavljevic, V.: Edge-preservation resolution enhancement with oriented wavelets. In: Proc. of the IEEE International Conference on Image Processing (ICIP), pp. 1252–1255 (2008)
Sun, J., Sun, J., Xu, Z.B., Shum, H.Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. on Image Processing 20(6), 1529–1542 (2011)
Dai, S.Y., Han, M., Xu, W., Wu, Y., Gong, Y.H., Katsaggelos, A.K.: Softcuts: a soft edge smoothness prior for color image super-resolution. IEEE Trans. on Image Processing 18(5), 969–981 (2009)
Daubechies, I.: Ten lectures on wavelets. SIAM, Philadelphia (1992)
Carey, W.K., Chuang, D.B., Hemami, S.S.: Regularity-preserving image interpolation. IEEE Trans. on Image Processing 8(9), 1293–1297 (1999)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using MATLAB. Publishing House of Electronics Industry, Beijing (2004)
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Xu, Y., Li, X.M., Suen, C.Y. (2011). Image Super-Resolution Based Wavelet Framework with Gradient Prior. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23678-5_50
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DOI: https://doi.org/10.1007/978-3-642-23678-5_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23677-8
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