# Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus
## 3 Experimental Results
### Comparison with the State-of-the-Art  
In this section, we compare the proposed method with state-of-the-art DFF methods, such as VAR [1], DFM [18], RDF [19], Composite Focus Measure (CFM) [38], PAD [39], and F-RDF [20]. For experimental evaluation, we use the LFSD dataset [28], the Lytro dataset [49], and the dataset from mobile [18]. Figure 14 shows depth and point-cloud results of VAR [1], RDF [19], PAD [39], F-RDF [20] and proposed method on \"Buddha\" sample. The proposed method provide more clean and accurate depth, especially around object boundaries. The last row shows a zoom-in part of the highlighted region where the proposed method has more accurate depth than others. Figure 15 shows peak signal-to-noise ratio (PSNR) between predicted depth and ground truth. In order to compute _PSNR_, the block first calculates the mean-squared error using Equation (12). Then, the block computes the _PSNR_ using Equation (13)  
\[MSE=\frac{\sum_{M,N}\left[I_{1}(m,n)-I_{2}(m,n)\right]^{2}}{M*N}, \tag{12}\]  
\[PSNR=10\log_{10}\frac{R^{2}}{MSE}. \tag{13}\]  
In Equations (12) and (13), \(M\) and \(N\) are the number of rows and columns in the input images. \(R\) is the maximum fluctuation (the maximum possible pixel value of the image) in the input image data type. This figure shows that quality of the proposed method outperforms state-of-the-art methods. Figure 14: Comparison of point cloud of the scene with state-of-the-art methods. First row shows depth map, second: point cloud results, and last row shows cropped (zoomed-in) region of the scene. The proposed method provide with correct depth value when most methods fail (at cropped region). Figure 16 shows qualitative performance of the proposed method on LFSD dataset [28] over the recent methods, such as VAR [1], RDF [19], PAD [39], and F-RDF [20]. Each sample has 5 to 12 frames of size = 1080 \(\times\) 1080. Our proposed method provided clean depth with detail and clean edges, especially on samples B (branch of the tree), D (wire on the window), F (boundary of the statue), and H (pens). Figure 17 shows results of VAR [1], RDF [19], PAD [39], F-RDF [20], and the proposed method on Lytro dataset [49]. Our proposed method gives clean depth results, especially with sample E around the boundary of the lock. We evaluated the proposed method on many challenging samples. In Figures 18a and 19, proposed method provide clean and detailed depth even on complex region of the scene. In Figure 19, the proposed method provided cleaner edges than other methods on all samples, and second sample has leaf that only our method could able to provide accurate depth. Figure 18a has the complex part  
Figure 16: Qualitative evaluation on LFSD dataset [28] with state-of-the-art methods. The top of each sample has the number of used frames. The proposed method provide more accurate depth with clean edges, even at a complex scene, such as shiny object (**G**), more clean details on (**B**,**D**,**F**–**H**), and proposed method kept structure of the scene on (**A**,**C**,**E**) samples better than other methods. Figure 15: Quantitative evaluation on ‘Buddha’ synthetic sample (with 25 frames) with state-of-the-art methods. From left to right: Variational DFF (VAR) [1], Ring Difference filter (RDF) [19], Composite Focus Measure (CFM) [38], PAD method of multipliers (PAD) [39], Fast and noise robust RDF (F-RDF) [20], proposed, ground-truth, and in-focus image. The proposed method perform better than recent methods that visible in the images and reflected in the peak signal-to-noise ratio (PSNR) (in dB) shown below the each depth result. Note that depth image and PSNR value for CFM [38] was cropped from original paper. of the scene that is highlighted; \"Toy\" sample has small details that the proposed method provided accurate depth with clean edges, and \"Buddha\" sample has a column that shows proposed method provided accurate depth, while other methods failed, and the proposed method has more a accurate result on the \"Flower\" sample with small details.