An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform(DWFT). The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all these region representations of all the source images, we produce a shared region representation to label all the input images. The fusion process is guided by these region representations. Region match measure of the source images is calculated for each region in the shared region representation. When fusing the similar regions, weighted averaging mode is performed; otherwise selection mode is performed. Experimental results using real data show that the proposed algorithm outperforms the traditional pyramid transform based or discrete wavelet transform (DWT) based algorithms in multi-sensor image fusion.

In this paper, an image fusion method is proposed based on the non-separable wavelet frame (NWF) for merging a high-resolution panchromatic image and a low-resolution multispectral image. The low- frequency part of the panchromatic image is directly substituted by multispectral image. As a result, the multispectral information of the multispectral image can be preserved effectively in the fused image. Due to multiscale method for enhancing the high-frequency parts of the panchromatic image, spatial information of the fused image can be improved. Experimental results indicate that the proposed method outperforms the intensity-hue-saturation (IHS) transform, discrete wavelet transform and separable wavelet frame in preserving spectral and spatial information.

In recent years face recognition has received substantial attention, but still remained very challenging in real applications. Despite the variety of approaches and tools studied, face recognition is not accurate or robust enough to be used in uncontrolled environments. Infrared (IR) imagery of human faces offers a promising alternative to visible imagery, however, IR has its own limitations. In this paper, a scheme to fuse information from the two modalities is proposed. The scheme is based on eigenfaces and probabilistic neural network (PNN), using fuzzy integral to fuse the objective evidence supplied by each modality. Recognition rate is used to evaluate the fusion scheme. Experimental results show that the scheme improves recognition performance substantially.

In this paper, a novel image fusion method based on the expectation maximization (EM) algorithm and steerable pyramid is proposed. The registered images are first decomposed by using steerable pyramid. The EM algorithm is used to fuse the image components in the low frequency band. The selection method involving the informative importance measure is applied to those in the high frequency band. The final fused image is then computed by taking the inverse transform on the composite coefficient representations. Experimental results show that the proposed method outperforms conventional image fusion methods.

Due to the limited depth-of-field of optical lenses, it is difficult to get an image with all objects in focus. One way to overcome this problem is to take several images with different focus points and combine them into a single composite which contains all the regions full focused. This paper describes a pixel-clarity-based multifocus image fusion algorithm. The characteristic of this approach is that the pixels of the fused image are selected from the clearest pixels in the input images according to pixel clarity criteria. For each pixel in the source images, the pixel clarity is calculated. The fusion procedure is performed by a selection mode according to the magnitude of pixel clarity. Consistency verification is performed on the selected pixels. Experiments show that the proposed algorithm works well in multifocus image fusion.