Image fusion is the process of synthesizing multiple images into one image, aiming to fuse information from different images to obtain more details and richer visual effects. In this article, we will introduce how to implement image fusion using Matlab.
Article directory
simple case
First, we need to understand the two main methods of image fusion: pixel-level fusion and feature-level fusion. Pixel-level fusion refers to the operation of each pixel, and the fusion of images through weighted average of pixel values or other algorithms. Feature-level fusion refers to the extraction and fusion of features in the image, such as edges and textures.
In Matlab, we can use the functions in the Image Processing Toolbox to achieve image fusion. The following is a basic image fusion process:
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Read image: Use the imread function to read the image to be fused. For example, we can read two images named "image1.jpg" and "image2.jpg" with the following code:
image1 = imread('image1.jpg');
image2 = imread('image2.jpg'); -
Image preprocessing: Preprocessing the image as needed. For example, you can adjust the size, contrast, brightness, etc. of the image. You can use functions such as imresize, imadjust, etc.
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Feature extraction: Feature extraction is performed on the image to obtain the features that need to be fused. For example, functions such as edge, texturefilt, etc. can be used to extract edge and texture features.
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Feature fusion: According to the importance and weight of the features, the features are fused. You can use a simple weighted average method, or you can use more complex algorithms such as wavelet transform, Laplacian pyramid, etc.
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Image reconstruction: According to the fused features, the final fused image is reconstructed. You can use functions such as imfuse, imlincomb, etc.
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Save the fused image: Use the imwrite function to save the fused image to the specified file. For example, use the following code to save the fused image as "fusion_image.jpg":
imwrite(fusion_image, 'fusion_image.jpg');
Through the above steps, we can realize the basic fusion of images. However, to implement more advanced image fusion algorithms, more functions and techniques may need to be used. For example, methods such as image pyramid and multi-scale decomposition can be used to achieve multi-scale fusion.
To sum up, using Matlab to achieve image fusion can be completed through steps such as image reading, preprocessing, feature extraction, feature fusion, image reconstruction, and saving the fused image. By using the functions in Matlab's image processing toolbox, we can achieve different types of image fusion, including pixel-level fusion and feature-level fusion. I hope this article can help you understand the implementation of image fusion in Matlab.
Source code + data set download
Multiple image fusion cases based on Matlab (source code + data set).rar: https://download.csdn.net/download/m0_62143653/88189908
