A novel context enhancement technique is presented to automatically combine images of the same scene captured at different times or seasons. A unique characteristic of the algorithm is its ability to extract and maintain the meaningful information in the enhanced image while recovering the surrounding scene information by fusing the background image. The input images are first decomposed into multiresolution representations using the Dual-Tree Complex Wavelet Transform (DT-CWT) with the subband coefficients modelled as Cauchy random variables. Then, the convolution of Cauchy distributions is applied as a probabilistic prior to model the fused coefficients, and the weights used to combine the source images are optimised via Maximum Likelihood (ML) estimation. Finally, the importance map is produced to construct the composite approximation image. Experiments show that this new model significantly improves the reliability of the feature selection and enhances fusion process.
|Translated title of the contribution
|Context enhancement through image fusion: a multiresolution approach based on convolution of Cauchy distributions
|Title of host publication
|IEEE International Conference on Acoustic, Speech and Signal Processes (ICASSP 2008), Las Vegas
|Institute of Electrical and Electronics Engineers (IEEE)
|1309 - 1312
|Number of pages
|Published - 2008
|2008 IEEE International Conference on Acoustics, Speech, and Signal Processing - Caesars Palace, Las Vegas, United States
Duration: 30 Mar 2008 → 4 Apr 2008
|2008 IEEE International Conference on Acoustics, Speech, and Signal Processing
|30/03/08 → 4/04/08
Bibliographical noteRose publication type: Conference contribution
Sponsorship: This work was supported by the Overseas Research Students Award
Scheme (ORSAS - Tao Wan), UK, and by the Greek General Secretariat for
Research and Technology under program EΠAN, Code 131-γ.
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- context enhancement
- image fusion
- Cauchy distribution
- wavelet decompostion