Color image fusion and enhancement based on variational model

A variational model is proposed for color image fusion and contrast enhancement simultaneously. It combines the geometry of the images with the coherence and correlation constraints, the perceptual enhancement and the regularity constraints into a variational framework. The gradient descent flow is applied to minimize the functional and the numerical scheme of PDEs is presented. The model is compared with the wavelet-based fusion approach visually and quantitatively. Experimental results on the multi-focus color images and the multi-spectral remote sensing images are shown and the effectiveness of the model is verified. For the multi-focus color images, the model can recover an everywhere-in-focus image while enhancing its contrast and for the multi-spectral remote sensing images, it improves the spatial resolution and its contrast while preserving the spectral quality effectively. Our model's computational complexity for one time step is only O(N).