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.

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 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.

In remote sensing community, IHS (intensity, hue, and saturation) transform is one of the most commonly used fusion algorithm. A study on IHS fusion indicates that the color distortion cannot be avoided. Meanwhile, wavelet decomposition has a property of frequency division in transform domain. And the statistical property of wavelet coefficient reflects those significant features. So, a united optimal fusion method, which using the statistical property of wavelet decomposition and IHS transform on pixel and feature levels, is proposed. That is, the high frequency of intensity component I is fused on feature level with multi-resolution wavelet in IHS space, and the low frequency of intensity component I is fused on pixel level with optimal weight coefficients. Spectral information and spatial resolution are two performance indexes of optimal weight coefficients. Experiment results show that it is a practical and effective method.