## 4 Experimental Results
### Evaluation  
#### 4.2.1 Evaluate under Noise-Free Conditions  
In this section, our network is trained on the noise-free training set. In order to validate the effectiveness of our algorithm, this paper compares our model with 13 state-of-the-art DSR methods, including Bicubic, JID [34], SRCNN [9], LapSRN [35], AR [36], VDSR [37], MSG-Net [23], MFR-SR [24], DRN [15], RDN-GDE [38], PAG-Net [39], PMBANet [40], and PDR-Net [41]. All comparison algorithms are evaluated using the same experimental configuration and training data. The experimental results are obtained based on the code and relevant data provided by the author. In this paper, the test images from the Middlebury datasets [32] are divided into three groups: A, B, and C. The root mean square error (RMSE) is used as the quantitative metric to evaluate the performance of the super-resolution network on the upsampled results. A smaller RMSE value indicates a better reconstruction quality of the depth maps. Due to the limitation of the resolution of the ground truth depth maps, the analysis of RMSE results in Group A is conducted only for upscaling factors of 2, 4, and 8. As shown in Tables 1-3, the RMSE values obtained from all state-of-the-art methods for various upsampling factors are listed, respectively. The best results are marked in bold font, while the second-best results are marked with an underline. Our network exhibits superior performance across almost all scaling factors. It can be seen from the table that in the DSR task, the method based on deep learning has a better reconstruction effect than the traditional method. Among the deep learning-based methods, networks such as MSG-Net [23], MFR-SR [24], and DRN [15] achieve better RMSE values compared to SRCNN [9] and LapSRN [35]. This is because the SRCNN [9] and LapSRN [35] networks only rely on LR depth maps for SR reconstruction. These networks are relatively simple and lack the incorporation of HR color images as prior information, making it difficult for the network to capture details, resulting in the loss of some details in the reconstructed depth map. In contrast, methods such as MSG-Net [23], MFR-SR [24], and DRN [15] incorporate the rich texture and structural information from HR color images, which enable them to better recover high-frequency details and improve the quality of the reconstructed depth maps. This highlights the importance of color image information in guiding DSR tasks. Specifically, Figure 4 shows the final depth map reconstruction results of different methods on 8\(\times\) upsampling on the Middlebury datasets. Visually, most methods demonstrate similar recovery results for large background areas. However, there are significant differences in details, such as the pens in Art or the anters in Reindeer. Among them, the Bicubic method yields the poorest result with significant image distortion and pronounced jagged edges; the SRCNN [9] method is superior to the traditional super-resolution reconstruction method. However, since this method takes the bicubic interpolated images as inputs, it not only increases the computational overhead but also introduces noise to the images. As a result, the images are affected by noise, leading to the problem of edge blurring; the LapSRN [35] method directly extracts features from the low-resolution (LR) images, avoiding the influence of noise. However, in some cases, it still encounters challenges in preserving fine details and edges, which may lead to slight blurring. Both MSG-Net [23] and MFR-SR [24] methods employ color image guidance for depth map reconstruction, thereby enhancing reconstruction quality and producing sharp depth edges. However,