Super-resolution reconstruction based on linear interpolation of wavelet coefficients
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High resolution image reconstruction is an image process to reconstruct a high resolution image from a set of blurred, degraded and shifted low resolution images. In this paper, the reconstruction problem is treated as a function approximation. We use linear interpolation to build up an algorithm to obtain the relationship between the detail coefficients in wavelet subbands and the set of low resolution images. We use Haar wavelet as an example and establish the connection between the Haar wavelet subband and the low resolution images. Experiments show that we can use just 3 low resolution images to obtain a high resolution image which has better quality than Tikhonov least-squares approach and Chan et al. Algorithm 3 in low noise cases. We also propose an error correction extension for our method which can lead to very good results even in noisy cases. Moreover, our approach is very simple to implement and very efficient.
KeywordsHigh resolution image reconstruction Haar Wavelet NormalShrink Local Wiener filtering
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- Ahuja, N., Lertrattanapanich, S., & Bose, N. K. (2005). Properties determining choice of mother wavelet. In IEEE proceedings-vision image and signal processing, Vol. 152, No. 5, Oct 2005, pp. 659–664.Google Scholar
- Chan, R., Chan, T., Ng, M., Tang, W., & Wong, C. (1998). Preconditioned iterative methods for high-resolution image reconstruction from multisensors. In F. Luk (Ed.), Proceedings to the SPIE symposium on advanced signal processing: Algorithms, architectures, and implementations, Vol. 3461, San Diego, CA, 1998, pp. 348–357.Google Scholar
- Connolly, T. J., & Lane, R. G. (1997). Gradient methods for superresolution. In Proceedings of the international conference on image processing, Vol. 1, Santa Barbara, California, 26–29 Oct. 1997, pp. 917–920.Google Scholar
- Kaltenbacher, E., & Hardie, R. C. (1996). High resolution infrared image reconstruction using multiple, low resolution, aliased frames. In O. Dayton (Ed.), Proceedings of the IEEE national aerospace electronics conference (NAECON), Vol. 2, USA, 1996, pp. 702–709.Google Scholar
- Kaur L., Gupta S., Chauhan R.C. (2002) Image denoising using wavelet thresholding. Third conference on computer vision, graphics and image processing, India, December 16–18: 2002Google Scholar
- Leung, K. T., & Tong, C. S. (2006). Super-resolution reconstruction using haar wavelet estimation. In Wavelet analysis and applications, ser. Applied and numerical harmonic analysis (pp. 419–430). Birkhauser Verlag Basel.Google Scholar
- Patti, A., Sezan, M., & Tekalp, A. (1993). Image sequence restoration and de-interlacing by motioncompensated kalman filtering. In Proceedings of SPIE, Vol. 1903, San Jose, CA, USA, 1993, pp. 59–70.Google Scholar
- Tekalp, A., Ozkan, M., & Sezan, M. (1992). High-resolution image reconstruction from lowerresolution image sequences and space-varying image restoration. In Proceedings of the IEEE international conference on acoustics, speech, and signal process, Vol. 3, San Francisco, CA, USA, 1992, pp. 169–172.Google Scholar
- Tsai R.Y., Huang T.S. (1984) Multiframe image restoration and registration. Advances in Computer Vision and Image Processing 1: 317–339Google Scholar