MATLAB Implementation of Image Fusion using PCA, Stationary and Discrete Wavelet Transform.

IMAGE FUSION:

Image Fusion is a process of combining the relevant information from a set of images of the same scene into a single image and the resultant fused image will be more informative and complete than any of the input images.

Input images could be multi sensor, multimodal, multi focus or multi temporal. There are some important requirements for the image fusion process:

- The fused image should preserve all relevant information from the input images

- The image fusion should not introduce artifacts which can lead to a wrong diagnosis

One of the important pre-processing steps for the fusion process is image registration. Image registration is the process of transforming different sets of data into one coordinate system. Image fusion find application in the area of navigation guidance, object detection and recognition, medical diagnosis, satellite imaging for remote sensing, military and civilian surveillance, etc. Image fusion algorithms can be categorized into different levels: pixel, feature, and decision levels. Pixel level fusion works directly on the pixels of source images while feature level fusion algorithms operate on features extracted from the source images.

 

PCA (Principal Component Analysis) Based Image Fusion:

Principal Component Analysis is a sub space method, which reduces the multidimensional data sets into lower dimensions for analysis. This method determines the weights for each source. Image using the eigenvector corresponding to the largest Eigen value of the covariance matrix of each source image.

 Fig: Block Diagram of PCA based Fusion

 

Discrete Wavelet Transform Method:

Wavelet transforms are multi-resolution image decomposition tool that provide a variety of channels representing the image feature by different frequency sub bands at multi-scale. It is a famous technique in analyzing signals. When decomposition is performed, the approximation and detail component can be separated 2-D Discrete Wavelet Transformation (DWT) converts the image from the spatial domain to frequency domain. The image is divided by vertical and horizontal lines and represents the first-order of DWT, and the image can be separated with four parts those are LL1, LH1, HL1 and HH1. 

Let s(n1,n2) is input image with size N1xN2 then scaling and wavelet function are

Steps:

1. Take input images of same size and of same scene or object taken from different sensor like visible and infra red images or images having different focus.

2. If the input images are color, separate their RGB planes to perform 2D transforms.

3. Apply one of the different image fusion technique.

4. Fuse the input image components by taking any of the pixels merging technique.

5. Resulting fused transform components are converted to image using inverse transform.

 

MATLAB Implementation of Image Fusion:

GUI Implementation: 1 Original View..............

GUI Implementation: 2 Image Fusion using Stationary Wavelet Transform..............

GUI Implementation: 3 Image Fusion with All Methods.........

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