Edge Suppression by Gradient Field Transformation Using Cross-Projection Tensors
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Measure of image sharpness using eigenvalues
Information Sciences: an International Journal
Theory of Dual-Tree Complex Wavelets
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
Since last few decades, multi sensor image fusion has been an emerging field of research in remote sensing, medical imaging and variety of computer vision applications. The primary objective of image fusion lies in the formation of a perceptually enhanced image from several multi sensor images using an appropriate fusion rule. The discrete wavelet transform DWT-based image fusion techniques have been popular due to less redundancy, low computations and perfect reconstruction with short support filters. But, it suffers severely from lack of directionality, shift variance, oscillations and aliasing problems. These issues have been overcome by means of Q-shift dual-tree complex wavelet transform DT-CWT-based image fusion. In this paper, an improved DT-CWT-based image fusion technique has been proposed to compose a resultant image with better perceptual as well as quantitative image quality indices. A bilateral sharpness based weighting scheme has been implemented for the high frequency coefficients taking both gradient and its phase coherence in account. A normalised maximum gradient weighting scheme is implemented for low frequency wavelet components. The fusion results demonstrate that the proposed fusion technique is more effective and competitive in terms of entropy, total standard deviation, average gradient measure and edge intensity measure.