Image Fusion Using Wavelet Transform and Fuzzy Reasoning

2012 ◽  
Vol 239-240 ◽  
pp. 1336-1339
Author(s):  
Xue Wen Ding ◽  
Berthold Mahundi ◽  
Fei Yang ◽  
Guang Quan Xu
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Fuzzy Reasoning ◽  
Complete Information ◽  
Weighted Averaging ◽  
Perceptual Quality ◽  
Fusion Algorithm ◽  
Composite Image ◽  
Imaging Sensors ◽  
Weight Coefficients

The image fusion algorithm discussed in this paper which utilizes wavelet decomposition and fuzzy reasoning combines images from diverse imaging sensors into a single composite image. It first decomposed source images through wavelet transform, computed the extent of each source image’s contribution through fuzzy reasoning using the area feature of source images, and then fused the coefficients through weighted averaging with the extents of each source images’ contributions as the weight coefficients. Experimental results indicate the final composite image may have more complete information content or better perceptual quality than any one of the source images.

Image fusion algorithm based on wavelet transform and fuzzy reasoning

2007 ◽  
Author(s):  
Jiapeng Wu ◽  
Zhaoxuan Yang ◽  
Xuewen Ding ◽  
Yang Chen ◽  
Zengmin Wang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Fuzzy Reasoning ◽  
Fusion Algorithm

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.

Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform

2017 ◽  
pp. 143-157
Author(s):  
Girraj Prasad Rathor ◽  
Sanjeev Kumar Gupta
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Signal To Noise Ratio ◽  
Fusion Rule ◽  
Weighted Averaging ◽  
Adaptive Fuzzy ◽  
Fused Image ◽  
Adaptive Fuzzy Logic ◽  
Multimodality Image

Image fusion based on different wavelet transform is the most commonly used image fusion method, which fuses the source pictures data in wavelet space as per some fusion rules. But, because of the uncertainties of the source images contributions to the fused image, to design a good fusion rule to incorporate however much data as could reasonably be expected into the fused picture turns into the most vital issue. On the other hand, adaptive fuzzy logic is the ideal approach to determine uncertain issues, yet it has not been utilized as a part of the outline of fusion rule. A new fusion technique based on wavelet transform and adaptive fuzzy logic is introduced in this chapter. After doing wavelet transform to source images, it computes the weight of each source images coefficients through adaptive fuzzy logic and then fuses the coefficients through weighted averaging with the processed weights to acquire a combined picture: Mutual Information, Peak Signal to Noise Ratio, and Mean Square Error as criterion.

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.

Sign in / Sign up

Export Citation Format

Share Document