A fast and reliable fusion algorithm is the key to the practical application of image fusion technology, so the research of the fast fusion algorithm with a good fusion effect is particularly important. Based on the multi-resolution analysis, this paper discusses the principles of the PCNN fusion algorithm, wavelet transform and Laplace pyramid algorithm, and then performs image fusion experiments. Experimental results show that the fusion effect of the wavelet transform and PCNN algorithm is significantly higher than that of the Laplace pyramid algorithm.
Multi-source remote sensing image fusion is a data processing technology that complements two or more remote sensing images taken from the same target of multiple sensors to obtain more accurate and perfect comprehensive images. Image fusion is not only an important part of remote sensing detection data processing, but also has a wide range of applications in environmental detection, precision agriculture, urban planning and other fields. Based on the method of component substitution, this paper discusses the image fusion methods of Brovey transform, IHS transformation, PCA transformation and weighted fusion, and performs fusion experiments and analyzes on four methods, and finally summarizes and makes prospects.
Handwritten digit recognition is a process of identifying 0-9 ten digits handwritten by human hands, and its related research has always been a hot topic in the field of machine learning classification. In order to explore the accuracy of the classification recognition of handwriting bodies by K nearest neighbor classifier and MLP multilayer perceptron, this paper first introduces the relevant algorithm principle and its research progress, and then experiments on K nearest neighbor classifier and MLP multilayer perceptron, and summarizes the relevant experimental data. Experiments show that in the K nearest neighbor algorithm, the classification accuracy is the highest when the number of neighbors K=3; For the MLP multilayer perceptron algorithm, the classification rate is higher when the number of neurons is larger, the number of iterations is 1000, and the learning rate is smaller.
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