Aiming at the problems of image fusion in spatial domain, such as the extraction of different image sources and difficulty in selecting fusion weights, a new spatial domain image fusion algorithm is proposed. Using the basic principle of matrix similarity, the infrared image matrix is diagonally transformed, the visible light image matrix is mapped on the main eigenvectors, the weighted fusion method is used to process the eigenvalue matrix, and the fusion matrix is diagonalized inversely transformed and reconstructed Fusion image. Experimental results show that while the algorithm fully retains the effective information of the source image, the overall grayscale of the fused image has been significantly improved, and it has a good image quality evaluation index and better visual effects.
Traditional NSCT-based image fusion algorithms usually first perform NSCT transformation on the original image, and then perform the fusion of different scale coefficients, without specific analysis of the original image. In this regard, an image fusion algorithm based on K-means clustering is proposed. First use the K-means algorithm clustering to classify images, and then perform NSCT decomposition on the segmented images to obtain low-frequency and high-frequency subband coefficients. According to the characteristics of the segmented image, an adaptive weighted fusion method is used to fuse the coefficients. Finally, the fusion coefficients are inversely transformed by NSCT to obtain the final fusion image. Experimental results show that the algorithm is more effective in preserving image texture information and improving contrast, and the objective quality evaluation results are also better.
Due to its unique advantages, infrared temperature measurement technology has been used more and more widely in medical auxiliary diagnosis, and the change of ambient temperature is an important factor affecting medical infrared temperature measurement components. This paper builds a simulation platform for ambient temperature. At a fixed distance, by changing the ambient temperature and the target temperature, a large amount of infrared temperature measurement data is collected, and a two-dimensional compensation model of the scene temperature variation is obtained by combining the infrared focal plane non-uniformity correction and the least squares fitting. The actual scene experiment proves that the temperature measurement accuracy and stability have achieved satisfactory results.
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