This paper proposes a novel image magnification method based on bilinear interpolation, wavelet, and partial differential equation (PDE) techniques. The image which is interpolated linearly is decomposed by wavelet into a low frequency component image and three high frequency component images, and then the three high frequency component images and the original image regarded as low-frequency component will be used for image magnification by invert wavelet transform. Finally, a PDE involving gray fidelity constraint item called improvement-self-snake mode is presented in post-processing of the magnified image. The experimental results show that the proposed linear interpolation-wavelet-PDE approach is indeed efficient and effective in image magnification. In addition, we also compare the signal-to-noise ratio (SNR) of the linear interpolation-wavelet-PDE magnification method with methods of linear interpolation, linear interpolation-wavelet, and wavelet-PDE. The simulating results show that the linear interpolation-wavelet-PDE method indeed outperforms the three kinds of image magnification approaches mentioned above.
[1]
Kian Kee Teoh,et al.
Investigation on several basic interpolation methods for the use in remote sensing application
,
2008,
2008 IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications.
[3]
Stéphane Mallat,et al.
Multifrequency channel decompositions of images and wavelet models
,
1989,
IEEE Trans. Acoust. Speech Signal Process..
[4]
Thierry Blu,et al.
Linear interpolation revitalized
,
2004,
IEEE Transactions on Image Processing.
[6]
A. Willsky,et al.
A PDE approach to image smoothing and magnification using the Mumford-Shah functional
,
2000,
Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154).
[7]
Guofang Tu,et al.
Remote sensing image processing using wavelet fractal interpolation
,
2005,
ICCCAS 2005.