Multi-sensor image fusion using discrete wavelet frame transform

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.     

Image fusion based on expectation maximization algorithm and steerable pyramid

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.     

Modified maximum likelihood registration based on information fusion

The bias estimation of passive sensors is considered based on information fusion in multi-platform multi- sensor tracking system.The unobservable problem of bearing-only tracking in blind spot is analyzed.A modified maximum likelihood method,which uses the redundant information of multi-sensor system to calculate the target position,is investigated to estimate the biases.Monte Carlo simulation results show that the modified method eliminates the effect of unobservable problem in the blind spot and can estimate the biases more rapidly and accurately than maximum likelihood method.It is statistically efficient since the standard deviation of bias estimation errors meets the theoretical lower bounds.     

Visual remote sensing image fusion using local correlation moment

In this paper a fusion method is proposed for merging a high-resolution panchromatic image and a lowresolution multispectral image. The algorithm is based on discrete wavelet transform (DWT). It uses correlation moment rule to the low frequency bands and local deviation rule to the high frequency bands separately. Experimental results indicate that the proposed approach outperforms the traditional methods.     

Transfer color to night vision images

Night vision technology enables human beings to operat at night. Low light level imaging and infrared imaging are two key technologies in night vision field. However the images obtained through the night vision imaging systems are monochromatic, this hinders observers from interpreting the scene well. In order to improve targe detection and identification abilities, color night vision technology is being pursued as an effective method fo solving the problem. The main methods of realizing color…     

Color fusion of SAR and FLIR images using a natural color transfer technique

Fusion of synthetic aperture radar (SAR) and forward looking infrared (FLIR) images is an important subject for aerospace and sensor surveillance. This paper presents a scheme to achieve a natural color image based on the contours feature of SAR and the target region feature of FLIR so that the overall scene recognition and situational awareness can be improved. The SAR and FLIR images are first decomposed into steerable pyramids, and the contour maps in the SAR image and the region maps in the FLIR image are calculated. The contour and region features are fused at each level of the steerable pyramids. A color image is then formed by transferring daytime color to the monochromic image by using the natural color transfer technique. Experimental results show that the proposed method is effective in providing a color fusion of SAR and FLIR images.     

Fusion of visible and infrared imagery for face recognition

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.     

Visual remote sensing image fusion using local correlation moment

In this paper a fusion method is proposed for merging a high-resolution panchromatic image and a low resolution multispectral image.The algorithm is based on discrete wavelet transform(DWT).It uses correlation moment rule to the low frequency bands and local deviation rule to the high frequency bands separately.Experimental results indicate that the proposed approach outperforms the traditional methods.     

Pixel-clarity-based multifocus image fusion

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 theminto 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 fusedimage are selected from the clearest pixels in the input images according to pixel clarity criteria.For eachpixel in the source images,the pixel clarity is calculated.The fusion procedure is performed by a selectionmode according to the magnitude of pixel clarity.Consistency verification is performed on the selectedpixels.Experiments show that the proposed algorithm works well in multifocus image fusion.