According to recent advances in digital image processing techniques, interest in high-quality images has been increased. This paper presents a resolution enhancement (RE) algorithm based on the pyramid structure, in which Laplacian histogram matching is utilized for high-frequency image prediction. The conventional RE algorithms yield blurring near-edge boundaries, degrading image details. In order to overcome this drawback, we estimate an HF image that is needed for RE by utilizing the characteristics of the Laplacian images, in which the normalized histogram of the Laplacian image is fitted to the Laplacian probability density function (pdf), and the parameter of the Laplacian pdf is estimated based on the Laplacian image pyramid. Also, we employ a control function to remove overshoot artifacts in reconstructed images. Experiments with several test images show the effectiveness of the proposed algorithm.
Pyramid Probability Density Function Digital Image Processing Image Processing Technique Pyramid Structure
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Park SC, Park MK, Kang MG: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 2003, 20(3):21-36. 10.1109/MSP.2003.1203207CrossRefGoogle Scholar
Jain AK: Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 1989.MATHGoogle Scholar
Gonzalez RC, Woods RE: Digital Image Processing. 2nd edition. Prentice-Hall, Upper Saddle River, NJ, USA; 2002.Google Scholar
Li X, Orchard MT: New edge-directed interpolation. IEEE Transactions on Image Processing 2001, 10(10):1521-1527. 10.1109/83.951537CrossRefGoogle Scholar
Biancardi A, Cinque L, Lombardi L: Improvements to image magnification. Pattern Recognition 2002, 35(3):677-687. 10.1016/S0031-3203(01)00034-6MATHCrossRefGoogle Scholar
Leu J-G: Sharpness preserving image enlargement based on a ramp edge model. Pattern Recognition 2001, 34(10):1927-1938. 10.1016/S0031-3203(00)00117-5MATHMathSciNetCrossRefGoogle Scholar
Wang Q, Ward R: A contour-preserving image interpolation method. Proc. IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 3: 673-676.Google Scholar
Greenspan H, Anderson CH, Akber S: Image enhancement by nonlinear extrapolation in frequency space. IEEE Transactions on Image Processing 2000, 9(6):1035-1048. 10.1109/83.846246MATHMathSciNetCrossRefGoogle Scholar
Takahashi Y, Taguchi A: An arbitrary scale image enlargement method with the prediction of high-frequency components. Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 2003, 86(8):41-51. 10.1002/ecjc.10018CrossRefGoogle Scholar
Freeman WT, Jones TR, Pasztor EC: Example-based super-resolution. IEEE Computer Graphics and Applications 2002, 22(2):56-65. 10.1109/38.988747CrossRefGoogle Scholar
Sun J, Zheng N-N, Tao H, Shum H-Y: Image hallucination with primal sketch priors. Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR '03), June 2003, Madison, Wis, USA 2: 729-736.Google Scholar
Burt PJ, Adelson EH: The Laplacian pyramid as a compact image code. IEEE Transactions on Communications 1983, 31(4):532-540. 10.1109/TCOM.1983.1095851CrossRefGoogle Scholar