An Improved Image Fusion Algorithm Based on IHS and Wavelet Transform

2013 ◽  
Vol 427-429 ◽  
pp. 1641-1644
Author(s):  
Liu Ming ◽  
Shu Hui Li
Keyword(s):  
Wavelet Transform ◽  
High Resolution ◽  
Image Fusion ◽  
Wavelet Transformation ◽  
Experiment Research ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Spectral Image ◽  
Panchromatic Image ◽  
Ihs Transform

A new improved image fusion algorithm is proposed for multi-spectral image (MUL) and the high-resolution panchromatic image (PAN) based on intensity-hue-saturation (IHS) transform combined with wavelet transformation (WT). Firstly, the multi-spectral image is transformed into the IHS space for getting the intensity component (I).Then the high-resolution panchromatic image and I were matched with histogram. Secondly, the PAN and I were decomposed respectively by WT and fused to obtain the new I by the inverse WT. Finally, the fusion image was obtained by inverse IHS transform. four evaluate indicators are defined in this paper. By experiment research, the results show that this new method can effectively improve the fusion effect.

An Image Fusion Algorithm Based on Redundant Wavelet Transform

2014 ◽  
Vol 687-691 ◽  
pp. 3656-3661
Author(s):  
Min Fen Shen ◽  
Zhi Fei Su ◽  
Jin Yao Yang ◽  
Li Sha Sun
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Source Image ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Imaging Characteristics ◽  
Focus Image ◽  
Redundant Wavelet Transform

Because of the limit of the optical lens’s depth, the objects of different distance usually cannot be at the same focus in the same picture, but multi-focus image fusion can obtain fusion image with all goals clear, improving the utilization rate of the image information ,which is helpful to further computer processing. According to the imaging characteristics of multi-focus image, a multi-focus image fusion algorithm based on redundant wavelet transform is proposed in this paper. For different frequency domain of redundant wavelet decomposition, the selection principle of high-frequency coefficients and low-frequency coefficients is respectively discussed .The fusion rule is that,the selection of low frequency coefficient is based on the local area energy, and the high frequency coefficient is based on local variance combining with matching threshold. As can be seen from the simulation results, the method given in the paper is a good way to retain more useful information from the source image , getting a fusion image with all goals clear.

The Research of LandCover Classification on Optical Image and Radar Image

2012 ◽  
Vol 500 ◽  
pp. 330-334
Author(s):  
Yin Xuan Cao ◽  
Zheng Zhao
Keyword(s):  
High Resolution ◽  
Classification Accuracy ◽  
Radar Image ◽  
Hengduan Mountains ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Spectral Image ◽  
Visual Effect ◽  
Landcover Classification ◽  
Better Than

This paper performed the fusion test using high resolution TerraSAR and ALOS optical multi-spectral image in Hengduan mountains area. The results of automatic classification compared to the visual effect for fusion image indicated that the classification accuracy by HPF is better than other fusion algorithm, which are superior to HPF in other application.

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

Polarimetric and Multi-spectral Image Fusion Based on HSI Color System and Wavelet Transform

2010 ◽  
Vol 39 (s1) ◽  
pp. 43-47 ◽  
Author(s):  
陈振跃 CHEN Zhen-yue ◽  
王霞 WANG Xia ◽  
邹晓风 ZOU Xiao-feng
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Spectral Image ◽  
Color System

Multi-Focus Image Fusion Using Local Energy Pattern

2011 ◽  
Vol 145 ◽  
pp. 119-123
Author(s):  
Ko Chin Chang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Local Energy ◽  
View Point ◽  
Fusion Algorithm ◽  
Image Capture ◽  
Focus Image ◽  
Better Than

For general image capture device, it is difficult to obtain an image with every object in focus. To solve the fusion issue of multiple same view point images with different focal settings, a novel image fusion algorithm based on local energy pattern (LGP) is proposed in this paper. Firstly, each focus images is decomposed using discrete wavelet transform (DWT) separately. Secondly, to calculate LGP with the corresponding pixel and its surrounding pixels, then use LGP to compute the new coefficient of the pixel from each transformed images with our proposed weighted fusing rules. The rules use different operations in low-bands coefficients and high-bands coefficients. Finally, the generated image is reconstructed from the new subband coefficients. Moreover, the reconstructed image can represent more detailed for the obtained scene. Experimental results demonstrate that our scheme performs better than the traditional discrete cosine transform (DCT) and discrete wavelet transform (DWT) method in both visual perception and quantitative analysis.

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