今天，试一试----->基于引导滤波和均值滤波的多聚焦图像融合---->实现。实现异源图像的融合。
- Today, try -> multi-focus image fusion based on guided filtering and mean filtering -> implementation.Realize the fusion of different images.
看看结果：
- result：
解释：
对于代码的实现，其实底层就是一个引导滤波器，基于引导滤波和均值滤波的多聚焦图像融合。看效果还是很不错的。但是是否适用于我们自己的数据集，还是得通过多种评价指标来判定。
图像融合的评价指标我进行了相应的总结：
-
Explanation:
For code implementation, in fact, the bottom layer is a guide filter, based on the guide filter and mean filter multi-focus image fusion.It looks pretty good.However, whether it is applicable to our own data set depends on a variety of evaluation indicators.
The evaluation indexes of image fusion are summarized as follows:
图像质量评价
图像算法评估
图像算法评估 = 定性（主观，观察） + 定量（客观，特征值） + 算法时间
定性：主要是观察+分析
定量：主要是各参数指标，又分为 参考质量+非参考质量
参考质量：处理后的图和原图之间的相关质量度量，比如对比度提升，轮廓复原率，过饱和率，结构相似度，
PSN, SSIM, RMSE and UIQ
非参考质量：图像本身的一些指标，
e, σ, r and CNR
-
Image quality evaluation
Image algorithm evaluation
Image algorithm evaluation = qualitative (subjective, observation) + quantitative (objective, eigenvalue) + algorithm time
Qualitative: basically be observation + analysis
Quantitative: mainly refers to each parameter index, which is also divided into reference quality + non-reference quality
Reference quality: Relative quality measures between the processed image and the original image, such as contrast enhancement, contour recovery rate, supersaturation rate, structural similarity,
PSN, SSIM, RMSE and UIQ
Non-reference quality: some indicator of the image itself,
E,, R and CNR
图像质量主观评价
绝对评价
相对评价
-
Subjective evaluation of image quality
Absolute evaluation
Relative evaluation
图像质量客观评价
- Objective evaluation of image quality
我已经实现了14中图像质量的评价算法，到时候根据自己的需求进行选择合适的评价算法。
-
I have implemented 14 image quality evaluation algorithms, and then I will choose the appropriate evaluation algorithm according to my own needs.
I hope I can help you,If you have any questions, please comment on this blog or send me a private message. I will reply in my free time.