## 2 Related Works
### Color Image-Guided DSR Technology  
Color image-guided depth map super-resolution technology often uses the color image corresponding to the depth image as guidance to improve the quality of the generated depth map. By leveraging the texture, color, and structural features present in the color image, these methods aim to recover fine details and improve the overall quality of the depth map. The color space mismatch between the color image and the depth map poses a challenge in color-guided depth map super-resolution techniques. How to fully fuse the color image guidance information with the depth image and avoid the generation of depth artifacts is an important problem faced by this method.  
[PERSON] et al. [5] propose the joint bilateral filtering for the first time, using the color high-resolution image as prior information during the upsampling process. By combining the spatial information of the low-resolution target image with the range information of the high-resolution guiding image, a better high-resolution depth image can be restored. [PERSON] et al. [6] propose a novel joint ternary filter that uses the updated edge map of the up-sampled depth map as guidance to change the filtering process. [PERSON] [21] is the first person to apply Markov Random Fields (MRF) to generate HR depth maps. He proposes a Markov random fields formulation, which consists of a data term based on the low-resolution depth map and a smooth term composition for the regularization of corresponding high-resolution color images. [PERSON] et al. [22] learn edge information from LR depth maps and HR color maps, and then recover high-resolution depth image using predicted edge image and HR color image.  
The depth super-resolution technology guided by color images based on deep learning has achieved remarkable results. [PERSON] et al. [23] propose a multi-scale guided convolutional network that uses intensity images as guidance, which can delicately restore high-quality depth images. [PERSON] et al. [24] adopt global residual learning and local residual learning to iteratively upsample LR depth maps under the guidance of HR intensity images. [PERSON] et al. [15] constructed a multi-scale progressive deep fusion network using the depth code branch and its related color code branch, which gradually merges the multi-scale feature maps extracted from the two branches to generate high-resolution depth images. [PERSON] et al. [25] propose a new attention-based multimodal attention fusion strategy, which makes full use of the low-level spatial information and high-level structural information, effectively exploring the complementarity of multi-level multimodal features. [PERSON] et al. [26] combine 3D depth images with 2D color images and use the combined image as prior information to guide depth image reconstruction. [PERSON] et al. [27] replace the original convolution kernel with a spatial variation kernel so that the HR color image can more effectively guide the LR depth map to restore a high-quality depth map. [PERSON] et al. [28] propose a single depth image super-resolution framework based on cross-task scene structure knowledge transfer for the first time. They introduce a knowledge distillation method that the depth estimation task and the super-resolution task are jointly trained. Although this multi-task learning approach can improve the performance of depth image super-resolution, it often comes with higher network complexity, which may require more powerful hardware to train the network effectively.