# Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
## 3 Experiment and Analyses
### Experimental Results and Discussions
#### 3.4.2 Evaluation on Lytro Image Dataset  
The Lytro image dataset was composed of 20 color multi-focus image pairs of the same size (\(520\times 520\)). For visual evaluation, the fused results of the \"Lytro17\" image pair obtained by different fusion methods are demonstrated in Figure 12. In order to observe the fusion effect of the transitional area more intuitively, some details of the puppy have been intercepted and enlarged. The DCHWT method still exhibited undesirable ringing artifacts around the head, as shown in Figure 12c. The same phenomenon can also be seen in Figure 12d,e,g. As shown in the close-up views of Figure 12f, the IM method suffered from severe blurring effects and false edges. Comparatively, the DMSR methods produced ideal fusion images without perceptible artifacts along the focus boundary.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Source** \\ **Images** \\ \end{tabular} & \begin{tabular}{c} **Metric** \\ **DCHWT [6]** \\ \end{tabular} & \begin{tabular}{c} **SOMP [19]** \\ \end{tabular} & \begin{tabular}{c} **GF [15]** \\ \end{tabular} & \begin{tabular}{c} **IM [16]** \\ \end{tabular} & \
\begin{tabular}{c} **CNN [30]** \\ \end{tabular} &
\begin{tabular}{c} **DMSR** \\ \end{tabular} \\ \hline \multirow{3}{*}{Flowerpot} & \(\begin{array}{c}Q_{MI}\\ Q^{AB/F}\end{array}\) & 0.8800 & 0.8642 & 0.9190 & 0.9112 & 0.9166 & **0.9182** \\ \cline{2-7}  & \(\begin{array}{c}Q_{MI}\\ Q^{AB/F}\end{array}\) & 0.8935 & 0.8142 & 1.0215 & 1.0969 & **1.1539** & 1.1248 \\  & \(\begin{array}{c}Q^{AB/F}\end{array}\) & 0.6706 & 0.6484 & 0.7252 & 0.7269 & 0.7326 & **0.7345** \\  & \(\begin{array}{c}VIFF\\ \end{array}\) & 0.8309 & 0.8450 & 0.8827 & 0.8891 & 0.8911 & **0.8913** \\ \hline \multirow{3}{*}{Bockcase} & \(\begin{array}{c}Q_{MI}\\ Q^{AB/F}\end{array}\) & 0.9266 & 0.7737 & 1.0271 & 1.0943 & 1.1104 & **1.1353** \\  & \(\begin{array}{c}Q^{AB/F}\end{array}\) & 0.6791 & 0.6374 & 0.7290 & 0.7246 & 0.7342 & **0.7357** \\  & \(\begin{array}{c}VIFF\\ \end{array}\) & 0.8600 & 0.8592 & 0.8820 & 0.8781 & 0.8800 & **0.9322** \\ \hline \multirow{3}{*}{Leaf} & \(\begin{array}{c}Q_{MI}\\ Q^{AB/F}\end{array}\) & 0.7825 & 0.5467 & 0.8843 & 0.9883 & 0.9055 & **1.0241** \\  & \(\begin{array}{c}VIFF\\ \end{array}\) & 0.7176 & 0.6447 & 0.7358 & **0.7386** & 0.7342 & 0.7353 \\  & \(\begin{array}{c}VIFF\\ \end{array}\) & 0.8049 & 0.8234 & 0.8071 & 0.8097 & 0.8103 & **0.8333** \\ \hline \multirow{3}{*}{Book} & \(\begin{array}{c}Q_{MI}\\ Q^{AB/F}\end{array}\) & 1.1069 & 0.8243 & 1.1702 & 1.2093 & 1.2331 & **1.2739** \\  & \(\begin{array}{c}VIFF\\ \end{array}\) & 0.7061 & 0.6273 & 0.7262 & 0.7228 & 0.7277 & **0.7279** \\  & \(\begin{array}{c}VIFF\\ \end{array}\) & 0.8501 & 0.8320 & 0.8497 & **0.8584** & 0.8496 & 0.8502 \\ \hline \multirow{3}{*}{Flower} & \(\begin{array}{c}Q_{MI}\\ Q^{AB/F}\end{array}\) & 0.9139 & 0.6207 & 1.0960 & 1.1206 & 1.1263 & **1.1404** \\  & \(\begin{array}{c}Q^{AB/F}\end{array}\) & 0.6969 & 0.6239 & 0.7175 & 0.7114 & **0.7183** & 0.7143 \\  & \(\begin{array}{c}VIFF\\ \end{array}\) & 0.9192 & 0.9150 & 0.9295 & 0.9243 & 0.9297 & **0.9318** \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 12: The “Lytro17” image pair and fusion results obtained by different fusion methods.  
Further, the quantitative assessments of the six methods are shown in Figure 13. The charts show that the proposed method outperformed the others and obtained the best quality metrics.  
Figure 13: Quantitative assessment line charts of different image fusion methods for Lytro image dataset: (**a**) \(Q_{M:}\) (**b**) \(Q^{AB/F}\); (**c**) visual information fidelity for fusion (_VIFF_).