Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in high-resolution (HR) RGB images from the same scene. The critical step of this task is to effectively extract domain-shared and domain-private RGB/depth features. In addition, three detailed issues, namely blurry edges, noisy surfaces, and over-transferred RGB texture, need to be addressed. In this paper, we propose the Spherical Space feature Decomposition Network (SSDNet) to solve the above issues. To better model cross-modality features, Restormer block-based RGB/depth encoders are employed for extracting local-global features. Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features. Shared features of RGB are fused with the depth features to complete the GDSR task. Subsequently, a spherical contrast refinement (SCR) module is proposed to further address the detail issues. Patches that are classified according to imperfect categories are input into the SCR module, where the patch features are pulled closer to the ground truth and pushed away from the corresponding imperfect samples in the spherical feature space via contrastive learning. Extensive experiments demonstrate that our method can achieve state-of-the-art results on four test datasets, as well as successfully generalize to real-world scenes. The code is available at \url{https://github.com/Zhaozixiang1228/GDSR-SSDNet}.

翻译：引导深度图超分辨率（GDSR）作为多模态图像处理领域的热点课题，旨在利用同一场景高分辨率（HR）RGB图像的附加信息对低分辨率（LR）深度图进行上采样。该任务的关键步骤在于有效提取域共享与域私有的RGB/深度特征。此外，需要解决三个细节问题：模糊边缘、噪声表面和过度迁移的RGB纹理。本文提出球面空间特征分解网络（SSDNet）以解决上述问题。为更好地建模跨模态特征，采用基于Restormer模块的RGB/深度编码器提取局部-全局特征，随后将提取的特征映射至球面空间以完成私有特征分离与共享特征对齐。RGB共享特征与深度特征融合完成GDSR任务。进而提出球面对比精炼（SCR）模块以进一步处理细节问题：将根据缺陷类别分类的图像块输入SCR模块，通过对比学习使该图像块的特征在球面特征空间中向真值靠拢，同时远离对应的缺陷样本。大量实验表明，本方法在四个测试数据集上取得了最优结果，并成功泛化至真实场景。代码见\url{https://github.com/Zhaozixiang1228/GDSR-SSDNet}。