# A Survey of Multi-Focus Image Fusion Methods
## 4 Limitations  
In the last ten years, multi-focus image fusion technology has been developed. However, there are still some urgent problems that need to be addressed.  
Most of the current fusion methods focus on feature extraction of source images, paying little attention to the image scene consistency, content deformation, and other registration problems. The actual source images are not as accurate as the experimental samples. Thus, the fusion effect would be greatly affected.  
In our view, the multi-view registration method can be studied to address the above problem. To be specific, capturing images of similar objects or scene from multiple perspectives can obtain a better representation of the scanned object. The multi-view registration  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Indicators** & **Evaluation of the Effect** \\ \hline \multirow{2}{*}{Information entropy (EN)} & Reflect the amount of information the image carries; the larger the value, the richer the amount of information, the better the quality. \\ \hline \multirow{2}{*}{Mean gradient (AG)} & Measure the clarity of the image, the greater the value, the higher the clarity, the better the quality. \\ \hline \multirow{2}{*}{MEAN} & Measure the average brightness of the image, the average is moderate, the better the quality. \\ \hline \multirow{2}{*}{Standard deviation (STD)} & Reflect the richness of image information, the larger the value, the more scattered the gray level distribution, the better the quality. \\ \hline \multirow{2}{*}{Mean square error (RMSE)} & Reflect the spatial details of the image, the smaller the value, the smaller the difference, the better the quality. \\ \hline \multirow{2}{*}{Signal to noise (SNR)} & To measure the proximity between fusion image and ideal image, the greater the value, the higher the similarity. \\ \hline \multirow{2}{*}{Normalized mutual information (QMI)} & Reflects the amount of information retained by the source image in the fusion image, the larger the value, the better the quality. \\ \hline \multirow{2}{*}{Structural similarity (SSIM)} & By comparing the structural similarity between source image and fusion image, the closer the value is to 1, the better the quality is. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Common evaluation indicators.  
can realized by various algorithms such as image mosaic, 3D model reconstruction from 2D image, etc.  
(2) Fusion efficiency  
Many scholars pursue the applicability and quality of fusion methods, but ignore the efficiency of fusion. However, we believe the fusion efficiency is of great value in practical application. The difficulty may be alleviated by immersing several fusion stages into a one-stop rapid stage, thus simplifying the sophisticated fusion process.  
(3) Application scenarios  
Although there are many multi-focus image fusion methods, most of them are studied and tested in public image libraries. We think it is helpful to collect and build image libraries in many specific industrial fields. Based on the specific image library, with the help of state-of-the-art mathematical theory or models, researchers can develop multi-focus image fusion methods suitable for actual application.