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- W2891757718 abstract "Image fusion is a way to merge high spatial resolution image with low-resolution image, which is extensively utilized in several vision-based applications. The primary objective is to improve the radiometric quality of fused image compared to multi-spectral images with low resolution. Existing methods are found to be efficient, but if the similar radiometric information is blended into every image, it produces redundant high frequency of pixels. Therefore, to overcome this issue, in this paper a Fuzzy and Gyrator Transform (GT) based image fusion technique is proposed. It decomposes remotely sensed image into the GT domain; then it preserves the radiometric data by using fuzzy if-then rules. In the last phase, proposed technique injects high-frequency blocks from input images and returns a high-resolution multi-spectral image with maximum radiometric data. In this work, to efficiently tune the required fuzzification parameters we have utilized well-known Genetic algorithm. Genetic algorithm iteratively tune the required parameters for fuzzification in such a way that the overall entropy is maximized. Therefore, entropy is used as fitness function. Quantitative analysis has shown that the proposed method has better structural detail, spatial resolution, and spectral information than existing methods." @default.
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- W2891757718 date "2018-08-24" @default.
- W2891757718 modified "2023-10-15" @default.
- W2891757718 title "Remote sensing image fusion using fuzzy logic and gyrator transform" @default.
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- W2891757718 doi "https://doi.org/10.1080/2150704x.2018.1500044" @default.
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