Guided depth super-resolution (GDSR) is an essential topic in multi-modal
image processing, which reconstructs high-resolution (HR) depth maps from
low-resolution ones collected with suboptimal conditions with the help of HR
RGB images of the same scene. To solve the challenges in interpreting the
working mechanism, extracting cross-modal features and RGB texture
over-transferred, we propose a novel Discrete Cosine Transform Network (DCTNet)
to alleviate the problems from three aspects. First, the Discrete Cosine
Transform (DCT) module reconstructs the multi-channel HR depth features by
using DCT to solve the channel-wise optimization problem derived from the image
domain. Second, we introduce a semi-coupled feature extraction module that uses
shared convolutional kernels to extract common information and private kernels
to extract modality-specific information. Third, we employ an edge attention
mechanism to highlight the contours informative for guided upsampling.
Extensive quantitative and qualitative evaluations demonstrate the
effectiveness of our DCTNet, which outperforms previous state-of-the-art
methods with a relatively small number of parameters. The code is available at
https://github.com/Zhaozixiang1228/GDSR-DCTNet.

提出一种新颖的离散余弦变换网络（DCTNet）来解决多模态图像处理中导向深度超分辨率（GDSR）的挑战，包括理解工作机制、提取跨模态特征和减弱 RGB 纹理过度传递，通过离散余弦变换模块、半耦合特征提取模块和边缘注意机制来分别解决这些问题。实验结果表明，DCTNet 的性能比之前最先进的方法更好，在参数数量相对较小的情况下能够更好地处理导向深度超分辨率。