Feature Preserving Image Interpolation and Enhancement Using Adaptive Bidirectional Flow

Authors: Shujun Fu, Qiuqi Ruan, Wenqia Wang

Abstract:

Image interpolation is a common problem in imaging applications. However, most interpolation algorithms in existence suffer visually to some extent the effects of blurred edges and jagged artifacts in the image. This paper presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to enhance edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove artifacts (''jaggies'') along the tangent directions. In order to preserve image features such as edges, angles and textures, the nonlinear diffusion coefficients are locally adjusted according to the first and second order directional derivatives of the image. Experimental results on synthetic images and nature images demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.

Keywords: anisotropic diffusion, bidirectional flow, directionalderivatives, edge enhancement, image interpolation, inverse flow, shock filter.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080046

References:

[1] Kenneth R Castleman. Digital Image Processing. Prentice Hall, 1995.
[2] S. W. Lee and J. K. Paik. Image interpolation using adaptive fast B-spline filtering. In Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 5, pp. 177-180, 1993.
[3] S. Carrato, G. Ramponi, and S. Marsi. A simple edge-sensitive image Interpolation filter. In Proc. IEEE Int. Conf. Image Processing, vol. 3,pp.711-714, 1996.
[4] K. Jensen and D. Anastassiou. Subpixel edge localization and the interpolation of still images. IEEE Trans. on Image Processing, vol. 4, pp. 285-295, Mar. 1995.
[5] J. Allebach and P. W. Wong. Edge-directed interpolation. In Proc. IEEE Int. Conf. Image Processing, vol. 3, pp. 707-710, 1996.
[6] S. Battiato, G. Gallo, F. Stanco. A locally adaptive zooming algorithm for digital images. Image Vision and Computing, Elsevier Science. Inc., Vol. 20, pp. 805-812, 2002;
[7] Xin Li, Michael T Orchard. New Edge-Directed Interpolation. IEEE transactions on image processing, 10(10):1521-1527, 2001.
[8] K Ratakonda, N Ahuja. POCS based adaptive image magnification. Proc. IEEE Int. Conf. Image Processing, vol. 3:203-207, 1998.
[9] Zhu Chang-Qing, Wang Qian, etc. Image Magnification Based on Multi-Band Wavelet Transformation. China Journal of Image and Graphics, 7(A)(3):653-656, 2003.
[10] D. A. Florencio and R. W. Schafer. Post-sampling aliasing control for natural images. In Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 2, pp. 893-896, 1995.
[11] G Aubert , P Kornprobst. Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, volume 147 of Applied Mathematical Sciences. Springer-Verlag, 2001.
[12] P Perona, J Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Machine Intell, 12(7): 629-639, 1990.
[13] G. Gilboa, N. Sochen, and Y.Y. Zeevi. A Forward-and-Backward Diffusion Process for Adaptive Image Enhancement and Denoising. IEEE Trans. Image Processing, vol. 11, no. 7, pp. 689-703, 2002.
[14] L. Alvarez and L. Mazorra. Signal and image restoration using shock filters and anisotropic diffusion. SIAM J. Numer. Anal., 31(2): 590-605, 1994.
[15] S. J. Osher and L. I. Rudin. Feature-oriented image enhancement using shock filters. SIAM J. Numer. Anal., vol. 27, pp. 919-940, 1990.
[16] Bryan S Morse, Duane Schwartzwald. Image magnification using level-set reconstruction. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.1:333-340, 2001.