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- W4310007318 abstract "A lot of human-generated data consist of images. While various tasks are becoming automated, reducing the complexity of image processing remains a challenge. At the same time, an image fusion algorithm can be applied before a convolutional neural network (CNN) as an image preprocessing method, where the image fusion combines incoming side-channel images into a single image. Thus, an image fusion can reduce the complexity of the conventional CNN task. However, traditional quality assessment functions (QAFs) for image fusion are a variety of calculation that does not provide a direct clue for the CNN accuracy of interest. In this study, we seek the correlation between QAFs and classification accuracy through CNN. The simulation result by training on differently color-fused CIFAR-10 datasets provides a possible standard to choose an image fusion method in the case of classifying fused images through a CNN. We expect the communication overhead to be decreased while using future image classification models in public." @default.
- W4310007318 created "2022-11-30" @default.
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- W4310007318 date "2022-10-19" @default.
- W4310007318 modified "2023-10-17" @default.
- W4310007318 title "Connecting Quality Metrics to Deep Learning Accuracy for Image Fusion Methods" @default.
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- W4310007318 doi "https://doi.org/10.1109/ictc55196.2022.9952709" @default.
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