# Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus
## 3 Experimental Results
### Qualitative and Quantitative Evaluation  
In order to evaluate our work, we use four different datasets: LFSD dataset [28], Lytro dataset [49], dataset from mobile [18], and our synthetic and medical (teeth) datasets. We compare our method with existing focus-measure focus-measure [29; 30; 31; 32; 33; 34; 35; 36; 37] and state-of-the-art DFF [1; 18; 19; 20; 22; 38; 39] methods. Figure 11 shows results on the LFSD dataset [28]. The LFSD dataset [28] provides focal stacks captured by Lytro Illum camera with corresponding depth map. It has 5 to 12 focus image frames for each subject. The depth map has darker intensity for farther depth. The proposed method estimates depth of each sample reasonably well. Table 2 shows quantitative evaluation of our method and previous focus measure methods (to get depth result we used our post-processing without filling textureless region) and state-of-the-art DFF methods. In order to evaluate our focus measurement without post-processing, we collected synthesized focus image set (focus level changed by adjusting camera focus) from graphics models, including ground truth depth. Figure 12 shows sample depth estimation results compared with ground truth. Depth images are normalized with min/max value corresponding to ground-truth, and depth images are from initial depth result applying median filter (size: \(3\times 3\)) without using post-processing. In this test, we vary patch size (Table 3). Each synthetic subject consists of 31 focus image frames with size \(405\times 720\). Accuracy in Table 3 is measured by average absolute difference of depth values. Each window size shows RMSE (Root Mean Squared Error) of the related sample, and the last row show average absolute difference of them. \(11\times 11\) window size shows best estimation accuracy, however it depends on the complexity of the shape of test objects. If objects have complicated surface shape, smaller window size will work better, but it may cause a noisy result (as shown \(5\times 5\) window size's result). \begin{table}
\begin{tabular}{c|l c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{4}{c}{**Lower Is Better**} & \multicolumn{4}{c}{**Accuracy (Higher Is Better)**} \\ \hline  & & **RMS** & **Log RMS** & **Abs rel** & **Sqr. rel** & \(\delta=\textbf{1.25}\) & \(\delta^{2}\) & \(\delta^{3}\) \\ \hline \multirow{4}{*}{**FSD**} & **GDER**[29] & 9.964 & 0.519 & 1.279 & 0.997 & 49.687 & 69.968 & 78.326 \\  & **LAPV**[30] & 9.608 & 0.417 & 1.553 & 0.922 & 56.028 & 76.290 & 82.680 \\  & **CONT**[31] & 9.890 & 0.503 & 1.523 & 0.988 & 52.363 & 71.991 & 80.187 \\  & **WAVS**[32] & 9.665 & 0.404 & 1.607 & 0.926 & 56.013 & 76.930 & 83.064 \\  & **WAVV**[32] & 9.668 & 0.391 & 1.575 & 0.921 & 56.153 & 77.204 & 83.848 \\  & **DCTE**[33] & 9.817 & 0.458 & 1.447 & 0.962 & 52.603 & 73.081 & 81.620 \\  & **WAVR**[34] & 9.684 & 0.401 & 1.583 & 0.928 & 56.090 & 76.807 & 83.336 \\  & **DCTR**[35] & 10.091 & 0.578 & 1.194 & 1.056 & 39.648 & 61.689 & 72.927 \\  & **LAPD**[36] & 9.706 & 0.419 & 1.585 & 0.944 & 55.878 & 76.445 & 82.596 \\  & **SFIL**[37] & 9.973 & 0.558 & 1.281 & 1.002 & 47.758 & 67.962 & 76.599 \\ \hline \multirow{4}{*}{**FSD**} & **VAR**[1] & 10.110 & 0.413 & 1.553 & 1.050 & 51.442 & 59.558 & 80.715 \\  & **RDF**[19] & 9.865 & 0.443 & 1.433 & 0.971 & 52.623 & 72.891 & 82.676 \\ \cline{1-1}  & **PAD**[39] & 10.227 & 0.506 & 1.178 & 1.113 & 43.446 & 62.380 & 74.864 \\ \cline{1-1}  & **F-RDF**[20] & 9.850 & 0.444 & 1.434 & 0.961 & 52.555 & 72.559 & 82.693 \\ \cline{1-1}  & **Proposed** & 8.915 & 0.338 & 1.223 & 0.843 & 63.199 & 80.653 & 86.495 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation on the LFSD dataset [28]. Figure 11: Experimental results on the LFSD dataset [28]. (**A**)—bench (6-frames), (**B**)—flower (10), (**C**)—lollipop-toy (7), (**D**)—flower-2 (9), (**E**)—flower-3 (12), (**F**)—marble-stone (12), (**G**)—door-handle (12), and (**H**)—liberty statue (10).