Hybrid method for multi-exposure image fusion based on weighted mean and sparse representation
Hybrid method for multi-exposure image fusion based on weighted mean and sparse representation
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DOI：
10.1109/eusipco.2015.7362495
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
T. Sakai;Daiki Kimura;Taichi Yoshida;M. Iwahashi
中科院分区：
文献类型：
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作者：
T. Sakai;Daiki Kimura;Taichi Yoshida;M. Iwahashi
本文提出了一种多曝光图像融合的混合方法。融合融合了一些图像捕捉相同的场景与不同的曝光时间，并产生一个高质量的图像。基于逐像素加权平均，已经积极地提出了许多方法，但是由于平均过程，它们得到的图像具有模糊的边缘和纹理。为了克服这一缺点，该方法分别融合输入图像的手段和细节。基于稀疏表示进行细节融合，融合结果保持了图像的清晰度。因此，所得到的融合图像具有清晰的边缘和纹理。通过仿真，我们表明，该方法优于以前的方法客观和感知。
We propose a hybrid method for multi-exposure image fusion in this paper. The fusion blends some images capturing the same scene with different exposure times and produces a high quality image. Based on the pixel-wise weighted mean, many methods have been actively proposed, but their resultant images have blurred edges and textures because of the mean procedure. To overcome the disadvantages, the proposed method separately fuses the means and details of input images. The details are fused based on sparse representation, and the results keep their sharpness. Consequently, the resultant fused images are fine with sharp edges and textures. Through simulations, we show that the proposed method outperforms previous methods objectively and perceptually.