# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 5 Conclusions and Future Directions  
This paper proposes an improved weighted average adaptive factor exposure assessment technique and local detail enhancement scheme. Different exposure evaluation strategies are adopted for different local brightness average values of multi-exposure image sequences, which effectively solves the problem that the fusion image cannot adapt to the local brightness of the input image sequence and the brightness and darkness are excessively unnatural. Use the K-means algorithm to divide the images into over-exposed images and low-exposure images according to the characteristics of the brightness histogram, the enhancement area is defined for each image using the brightness average value to reduce the enhanced pixels. In addition, a local Laplacian pyramid is employed to enhance the details in the brighter and darker areas. Through subjective and objective evaluation, the proposed method has achieved good results. However, the algorithm in this \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Image** & **SPD-MEF [19]** & **MRF [25]** & **DSIFT [28]** & **FEAF [7]** & **LE \({}^{*}\) [26]** & **B-LP [27]** & **LLF [14]** & **Ours** \\ \hline Balloons & 3.1730 & 3.2798 & 3.1199 & 3.5777 & 3.9937 & 4.2004 & 3.1292 & **3.0679** \\ Belgium house & 2.8407 & 2.8546 & 2.8917 & **2.6962** & 3.7687 & 4.7149 & 2.9922 & 2.7239 \\ Chinese garden & 1.9233 & 1.9612 & 1.9473 & 1.9023 & 3.7687 & 3.4454 & 1.9765 & **1.8895** \\ Church & 5.6679 & 5.4785 & 5.8223 & 5.5908 & - & 11.6606 & 6.127 & **5.4624** \\ House & 4.2175 & **3.4627** & 3.8084 & 3.9044 & 7.029 & 5.4794 & 3.9829 & 3.7875 \\ Kluki & 2.0643 & 2.0241 & 1.9835 & 2.0839 & 2.772 & 3.7827 & **1.9691** & 2.0995 \\ Lamp & 2.9679 & 3.1207 & 2.9444 & 3.2446 & 5.0047 & 5.1575 & 2.9170 & **2.8495** \\ Landscape & 2.5482 & 2.6530 & 2.6577 & 2.5973 & 3.3069 & 5.0838 & 2.6745 & **2.4388** \\ Laurenziana & 2.4811 & 2.4045 & 2.5069 & 2.3916 & - & 3.8703 & 2.4442 & **2.3915** \\ Lighthouse & 2.8888 & 2.7888 & 2.792 & 2.7928 & 3.7854 & 3.4865 & 2.9048 & **2.7841** \\ Mask & 2.8740 & 2.9096 & 2.8884 & **2.6709** & - & 4.3010 & 3.0076 & 2.9386 \\ Office & 3.1470 & 2.7835 & 2.9711 & 2.6890 & 4.6544 & 4.8830 & 2.9289 & **2.4686** \\ River & 3.3543 & 3.3329 & 3.2862 & 3.4824 & - & 4.5504 & 3.3380 & **3.2731** \\ Room & 2.6044 & **2.5601** & 2.7151 & 2.6818 & - & 3.2275 & 2.6519 & 2.8765 \\ Tower & 2.9965 & 2.3662 & 3.2855 & 2.2915 & 2.5976 & 4.1269 & 2.3101 & **2.1678** \\ Madison Capital & 2.2954 & 2.7648 & 2.3173 & **2.5553** & - & 5.0202 & 3.1901 & 2.7962 \\ Average & 3.0027 & 2.9215 & 2.9961 & 2.9470 & 4.0681 & 4.8119 & 3.0340 & **2.8759** \\ \hline \end{tabular} \({}^{*}\)LE fusion images are provided by [26]. \({}^{*}\)- means results not included in [26]. Bold indicates the best fusion result.  
\end{table} 
Table 4: Comparison of image quality assessment (NIQE) with existing MEF techniques. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Image** & **Number** & **SPD-MEF [19]** & **MRF [25]** & **DSIFT [28]** & **PEAF [7]** & **B-LP [27]** & **LLF [14]** & **Ours** \\ \hline Landscape & 3 & 1.36 & 0.32 & 0.45 & 0.90 & 23.55 & 1.23 & 2.35 \\ Lamp & 15 & 6.25 & 1.83 & 2.45 & 3.37 & 32.25 & 7.11 & 13.35 \\ Madison Capital & 30 & 14.65 & 3.57 & 5.20 & 5.48 & 50.71 & 18.11 & 23.54 \\ \hline \end{tabular} \({}^{*}\)LE fusion images are provided by [26]. \({}^{*}\)- means results not included in [26]. Bold indicates the best fusion result.  
\end{table} 
Table 5: Time (s) comparison of the proposed method with six existing methods. paper is inefficient when dealing with a large number of multi-exposure datasets, which also provides a direction for our next work. In recent years, the rapid development of deep learning has led to good application prospects in various fields. Therefore, in future work, we will try to apply neural networks to multi-exposure fusion algorithms, which are no longer limited to static multi-exposure images but are also applicable to dynamic multi-exposure images. Methodology, D.W.; software, C.X.