## 4 Experiment
### Depth Estimation Accuracy Analysis
#### 4.2.2 Quantitative Experimental Comparison and Analysis  
We selected the following three metrics to provide quantifiable and objective evaluation of the algorithm's depth recovery results, including Depth Pixel Density [35], Bad Pixel Ratio (BadPixel(0.5)) and Root Mean Square Error (RMSE) [34], with specific calculation formulas as follows:  
\[\text{DepthPixelDensity}=\frac{\sum_{i\in\Omega(\text{N})}\text{Pvalid}}{\sum_ {i\in\Omega(\text{N})}\text{Pvalid}+\text{Pinvalid}}, \tag{9}\]  
\[\text{BadPixel(0.5)}=\sum_{i\in\text{N}}\frac{|\text{error(i)}|}{|\text{N}|},\text{error(i)}=\begin{cases}1,\text{I}_{|\text{d}^{\text{tgt}}-\text{d}^{ \text{true}}_{1}|>0.5}\\ 0,\text{I}_{|\text{d}^{\text{tgt}}-\text{d}^{\text{true}}_{1}|<0.5}\end{cases}, \tag{10}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{N}}{\sum_{i=1}^{\text{N}}}(\text{d}^{\text {test}}_{i}-\text{d}^{\text{true}}_{i})^{2}}, \tag{11}\]  
Among these formulas: Depth Pixel Density represents the ratio of effective depth pixels to total pixels, Pvalid refers to effective pixels with reliable depth information, Pinvalid indicates invalid pixels with unreliable or missing depth information, N is the total number of pixels in the area, BadPixel(0.5) represents the proportion of abnormal pixels with errors greater than 0.5 pixels in total pixels, \(i\) is the index in a one-dimensional pixel array, \(\text{d}^{\text{test}}_{i}\) is the estimated depth value of the \(i\)-th pixel, \(\text{d}^{\text{true}}_{i}\) is the ground truth depth value at the \(i\)-th pixel, \(|\text{error(i)}|\) is the absolute value of depth estimation error, RMSE indicates the average deviation between predicted and true depth values.  
From the comparison results of three indicators in different dataset scenes in Table 2, we can observe:  
(1) Depth Pixel Density: Compared with Zeller algorithm and BLADE algorithm, Our algorithm increased Depth Pixel Density by at least 16.04% and 50.86% in HR260-Outdoor scenes, and by at least 5.48% and 28.03% in R32-Indoor. This is primarily because Our algorithm comprehensively considered neighboring image feature information based on different microlens blur characteristics, deriving relatively effective depth values. In R12-Indoor, compared with Zeller algorithm, Our algorithm increased Depth Pixel Density by at least 1.18%, but decreased compared to BLADE algorithm. This is because when processing near-range scene stereo objects, the pixel matching window cannot fully display microcamera feature information, reducing the accuracy of matching cost updates.  
(2) Bad Pixel Ratio: Relative to Zeller algorithm and BLADE algorithm, Our algorithm reduced error pixel ratio by at least 1.93% and 2.05% in three scene types. This is because Our algorithm performed geometric consistency checks based on microlens position relationships to eliminate mismatched points, obtaining a lower error pixel ratio.  
(3) Root Mean Square Error: Compared with Zeller algorithm and BLADE algorithm, Our algorithm reduced root mean square error by at least 3.55% and 3.99% in three scene types. This is because Our algorithm optimized depth information of weak texture region pixels through planar prior geometric relationships between microimages.