Matlab code for Neuro Fuzzy Based Image Fusion


This paper proposes anatomical image fusion based on second generation wavelet transform . The fusion rule is based on neuro-fuzzy.

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Image fusion is a data fusion technology which keeps images as main research contents. It refers to the techniques that integrate multi-images of the same scene from multiple image sensor data or integrate multi images of the same scene at different times from one image sensor. The image fusion algorithm based on Wavelet Transform which faster developed was a multi-resolution analysis image fusion method in recent decade. Wavelet Transform has good time-frequency characteristics. The experiments show that the method could extract useful information from source images to fused images so that clear images are obtained.



Image fusion is a technique of integrating all applicable and balancing information from images of similar source or various sources into a single merged image without any degradation. The main objective of medical image fusion is reliable integration of visual information observed from different input images into single image not including any degradation and loss of information. Where, the resulting image can give more creative information than any of the input images. These fused image information used in various applications such as Satellite imaging, Medical imaging, Robot vision, Multi-focus image fusion, Digital camera application etc. Three main fusion methods have been deal in the literature of image fusion – pixel level, feature level and decision level. Pixel-level is low level of image fusion, deals with pixels obtained at imaging sensor output. Feature-level fusion operation is performed on features extracted from source images. Decision-level deals with image descriptors.


Contrast Based Image Fusion

  • First Pyramid Structural Transform
  • The mean value of the local window of the approximate coefficient be the background of the central pixel of the corresponding local window of the detail component.
  • And the maximum coefficients of detail components are respectively taken as the most salient features with the corresponding local window along horizontal, vertical, and diagonal directions.
  • Then the new contrast is defined


  • DCT is not a suitable transform for edge information
  • For all medical images the contrast will be same so we can’t take contrast feature for Fusion


  • Using Wavelet Transform to decompose original images into proper levels. One low-frequency approximate component and three high-frequency detail components will be acquired in each level.
  • Lifting Transform of individual acquired low frequency approximate component and high frequency detail components from both of images, neighborhood interpolation method is used and the details of gray can’t be changed.
  • According to definite standard to fuse images, local area variance is chose to measure definition for low frequency component.
  • Inverse Transformation is taken to get Original Image 




  • Matlab 7.115 above versions


  • Medical Application
  • Satellite & Research Application
  • Digital Camera Application
  • War Field Applications


This paper has shown that every image fusion technique has its own benefits and limitations. As most of the existing methods are based upon transform domain therefore it may results in some color artifacts which may reduce the performance of the transform based vision fusion methods. It is also found that the problem of the uneven illuminate has also been neglected in the most of existing work on fusion. Most of the existing work has focused on gray scale images not much work is done for color images.


[1] R.Vijayarajan, Iterative block level principal component averaging medical image, fusion Optik 125 (2014) 4751– 4757.

[2] Qingping Li, Region-based Multi-focus Image Fusion Using the Local Spatial Frequency, IEEE 25th Chinese Control and Decision Conference (CCDC), 3792-3796, 2013.

[3] Huaxun Zhang, A Way of Image Fusion Based on Wavelet Transform, IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, 498-501, 2013.

[4] Rishu Garg, Survey on Multi-Focus Image Fusion Algorithms, RAECS UIET Panjab University Chandigarh, 06 – 08 March, 2014.

[5] Wang Yang, Research and development of medical image fusion, IEEE North Eastern University, Shenyang, 307-309, 2013.

[6] Mohammed Hossny, Image Fusion Metrics: Evolution in a Nutshell, UK Sim 15th International Conference on Computer Modelling and Simulation, 443-450, 2013.

[7] Mingjing Li, Review on Technology of Pixel-level Image Fusion, 2nd International Conference on Measurement, Information and Control, 341-344, 2013.

[8] Rong Fan, Non-linear Weighted Multiband Fusion Image Algorithm, IEEE Workshop on Electronics, Computer and Applications, 449-452, 2014


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