Traditional Image Fusion Algorithms Research Articles

An Image Fusion Method Based on Shearlet and Multi-Decision

Image processing is the basis of computer vision. Aiming at some problems existed in the traditional image fusion algorithm, a novel algorithm based on shearlet and multi-decision is proposed. At first we discussed multi-focus image fusion and then we use Shearlet transform and multi-decision for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach retains image detail and more clarity.

Image Fusion Based on Shearlet and Multi-Decision

Image Fusion is an important and useful subject in Image Processing and Computer Vision. The traditional image fusion algorithm could not provide satisfactory fusion results. Aiming to solving this problem, in this paper, we proposed an algorithm based on shearlet and multi-decision. First we discussed the application of the shearlet transform. Then we use difference decision rules for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach can provide more satisfactory fusion outcome.

Fusion of SAR Image Using Stationary Contourlet Transform

The stationary contourlet transform is built upon nonsubsampled pyramids and nonsubsampled directional filter banks and provides a shift invariant directional multiresolution image representation. Firstly, several SAR images can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions using the stationary contourlet transform. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of horizontal and vertical direction gradient information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused contourlet coefficients. Experimental results show that the proposed algorithm gives more satisfactory results than the traditional image fusion algorithms in preserving the edges and texture information.

Multi-focus Image Fusion Based on Region Segmentation

We proposed a new image fusion algorithm based on region segmentation. The algorithm divided the adjacent region from the image through the edge detection, then using wavelet transform with different high-frequency fusion method in each sub-region, to retain the edge information of the source image. The experimental results showed that the image fusion algorithm based on region segmentation can overcome the drawbacks of single segmentation rule and processing in specific part, and the effect is better than the traditional wavelet-based image fusion algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3521

A multi-focus image fusion algorithm based on an improved dual-channel PCNN in NSCT domain

This paper presents a multi-focus image fusion algorithm based on dual-channel PCNN in NSCT domain. The fusion algorithm based on multi-scale transform is likely to produce the pseudo-Gibbs effects and it is not effective to fuse the dim or partial bright images. To solve these problems, this algorithm will get a number of different frequency sub-image of the two images by using the NSCT transform, the selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail, and the images are fused based on the improved dual-channel PCNN in order to determine the band-pass sub-band coefficient, at last fused image is obtained by using the inverse NSCT transform. Fusion rules based on dual-channel PCNN are used to solve the complexity of the PCNN parameter settings and long computing time problems. The experimental results show that the algorithm has overcome the defects of the traditional multi-focus image fusion algorithm and improved the fusion effect.

A Novel Multi-Focus Image Fusion Method Using Shearlet Transform

According to the different frequency areas decomposed by shearlet transform, the selection principles of the lowpass subbands and highpass subbands were discussed respectively. The lowpass subband coefficients of the fused image can be obtained by means of the fusion rule based on the region variation, the highpass subband coefficients can be selected by means of the fusion rule based on the region energy. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach can provide more satisfactory fusion outcome.