含噪声图像的多聚焦融合算法

Multi-focus fusion algorithm for noisy images

由于常用的多聚焦融合算法不能很好地区分噪声和视觉上有意义的特征信息,本文提出了一种有效抑制图像噪声干扰的多聚焦图像融合算法。采用改进的基于自适应分块的图像融合算法并结合一种新的抗噪性好的清晰度评价算子有效解决了含噪图像的融合问题,取得了理想的图像融合效果。对融合图像的均方根误差和互信息进行了定量比较,结果表明,在不同强度的噪声干扰下,与对比度金字塔、小波变换和Contourlet变换方法相比,所提方法的平均均方根误差分别降低了4.288 9,4.479 1和4.187 1;平均互信息分别提高了2.366 4,3.282 5和2.063 9。本算法在噪声干扰下仍能准确地保持图像的有用信息,有效抑制噪声的影响,得到了比传统融合方法更优的视觉效果。

This paper attempts to improve the commonly used image multi-focus fusion methods,for they could not identify meaningful image features from noises.An antinoise multi-focus image fusion algorithm is presented.The improved adaptive block-based image fusion algorithm combined with a new focus measure with noise immunity is used to focus the noisy image effectively and to achieve good fusion results.Root Mean Square Error（RMSE） and Mutual Information（MI） are selected to evaluate the fused noisy image with different intensities and comparison experiments are performed.As compared with those of contrast pyramid,wavelet transform and Contourlet transform,the average RMSE of the fused image by the proposed method has been decreased by 4.288 9,4.479 1 and 4.187 1 respectively,while the average MI increased by 2.366 4,3.282 5 and 2.063 9,respectively.With the noise interference,the proposed method can maintain the useful information of the source images accurately,suppress noise effects effectively and obtain better image fusion quality.