Abstract
Image fusion is a visual enhancement technique that combines source images from different sensors to produce a more robust and informative fused image for subsequent processing or decision making. Infrared and visible light images share complementary properties that enable the production of robust and informative fused images. This paper proposed an infrared and visible image fusion method that improved the tetrolet framework to improve infrared and visible image fusion quality. First, the source image is enhanced by bicubic interpolation. The improved tetrolet transform then decomposes the enhanced source image; the high-frequency components are fused by convolutional sparse representation theory and combined with corresponding rules, and the low-frequency components are fused by defining ISER descriptors. Finally, we use the inverse transform to reconstruct the fused image. Qualitative and quantitative experimental results on five groups of typical infrared and visible image datasets demonstrate the proposed method's effectiveness. The proposed method exhibits better performances on subjective vision and objective indexes compared with the other state-of-the-art methods.
Highlights
A wave of information has swept the world and has penetrated many fields, including the military, agriculture, industry, and education
This paper proposes a method for the fusion of infrared and visible images based on the tetrolet transform combined with convolutional sparse representation based on the above drawbacks
The algorithm mainly consists of the following steps: Infrared and visible image fusion based on tetrolet transform and convolutional sparse representation
Summary
A wave of information has swept the world and has penetrated many fields, including the military, agriculture, industry, and education. This paper proposes a method for the fusion of infrared and visible images based on the tetrolet transform combined with convolutional sparse representation based on the above drawbacks This solution first uses the bicubic interpolation method to enhance the resolution of the source infrared and visible light images, so that the resolution of the image to be fused is higher; uses the improved Tetrolet transform to decompose the image to be fused to obtain more concentrated high-frequency and low-frequency coefficients; In the low-frequency coefficient part, the convolution sparse representation is used for secondary decomposition to better extract the significant structure, area and target characteristics of the low-frequency coefficient, so as to obtain a better low-frequency coefficient fusion effect; in the high-frequency coefficient fusion part, according to the high frequency The characteristics of the coefficients use indicators such as information entropy, direction frequency, energy gradient, and range filter descriptors to establish the ISER descriptor, which can better reflect the characteristics of high-frequency coefficients and obtain better high-frequency coefficient fusion results.
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