利用基于多群体差分进化优化的曲线小波变换增强平移锐化功能

Mustafa Hüsrevoğlu, Ahmet Emin Karkınlı
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引用次数: 0

摘要

在本研究中,采用了平锐化处理,将低分辨率 RGB 图像的色彩信息与高分辨率全色图像的细节信息合并,以获得更高质量的图像。在此过程中,使用 Curvelet 变换方法和基于多种群的差分进化(MDE)算法对权重进行了优化。所提出的方法在 Landsat ETM 卫星图像上进行了测试。对于 Landsat ETM 数据,RGB 图像的分辨率为 30 米,而全色图像的分辨率为 15 米。为了评估该研究的性能,将所提出的基于 MDE 优化曲线变换的平锐化方法与经典的 IHS、Brovey、PCA、Gram-Schmidt 和 Simple Mean 方法进行了比较。比较过程采用了 RMSE、SAM、COC、RASE、QAVE、SID 和 ERGAS 等指标。结果表明,所提出的方法在视觉质量和数值精度方面都优于传统方法。
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Enhanced Pansharpening Using Curvelet Transform Optimized by Multi Population Based Differential Evolution
In this study, a pansharpening process was conducted to merge the color information of low-resolution RGB images with the details of high-resolution panchromatic images to obtain higher quality images. During this process, weight optimization was performed using the Curvelet Transform method and the Multi Population Based Differential Evolution (MDE) algorithm. The proposed method was tested on Landsat ETM satellite image. For Landsat ETM data, the RGB images have a resolution of 30m, while the panchromatic images have a resolution of 15m. To evaluate the performance of the study, the proposed MDE-optimized Curvelet Transform-based pansharpening method was compared with classical IHS, Brovey, PCA, Gram-Schmidt and Simple Mean methods. The comparison process employed metrics such as RMSE, SAM, COC, RASE, QAVE, SID, and ERGAS. The results indicate that the proposed method outperforms classical methods in terms of both visual quality and numerical accuracy.
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