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Multiresolution Image Fusion Approach for Image Enhancement

机译:用于图像增强的多分辨率图像融合方法

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Image fusion is performed by combining the data from multiple spectrums i.e. red, blue, Near Infra Red (NIR) and green, which results in enhanced image. Line features are clear in the blue and green bands while the red band reveals vein structures. The NIR band shows the palm vein structure as well as partial line information. Multispectral imaging has been employed to acquire more discriminating information. In wavelet transform the features gets affected for limited number of coefficients. The discontinuities across a simple curve affects all the wavelet coefficients on the curve. The advantage of the Curvelet transform is to handle curves using only a small number of coefficients. We can obtain the better fusion efficiency for the fusion of curved shapes using Curvelet transform. Fusion results were evaluated and compared according to different measures of performance. These performance measures show that curvelet based image fusion algorithm provides better fused images than wavelet.
机译:通过合并来自多个光谱(即红色,蓝色,近红外(NIR)和绿色)的数据来执行图像融合,从而获得增强的图像。线特征在蓝色和绿色带中清晰可见,而红色带则显示出静脉结构。 NIR波段显示手掌静脉结构以及部分线信息。多光谱成像已被用来获取更多的区分信息。在小波变换中,有限数量的系数会影响特征。简单曲线上的不连续性会影响曲线上的所有小波系数。 Curvelet变换的优点是仅使用少量系数即可处理曲线。使用Curvelet变换可以为曲面形状的融合获得更好的融合效率。根据不同的性能指标对融合结果进行评估和比较。这些性能指标表明,基于Curvelet的图像融合算法提供的融合图像比小波更好。

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