  Guided depth super-resolution (GDSR) is a hot topic in multi-modal image
processing. The goal is to use high-resolution (HR) RGB images to provide extra
information on edges and object contours, so that low-resolution depth maps can
be upsampled to HR ones. To solve the issues of RGB texture over-transferred,
cross-modal feature extraction difficulty and unclear working mechanism of
modules in existing methods, we propose an advanced Discrete Cosine Transform
Network (DCTNet), which is composed of four components. Firstly, the paired
RGB/depth images are input into the semi-coupled feature extraction module. The
shared convolution kernels extract the cross-modal common features, and the
private kernels extract their unique features, respectively. Then the RGB
features are input into the edge attention mechanism to highlight the edges
useful for upsampling. Subsequently, in the Discrete Cosine Transform (DCT)
module, where DCT is employed to solve the optimization problem designed for
image domain GDSR. The solution is then extended to implement the multi-channel
RGB/depth features upsampling, which increases the rationality of DCTNet, and
is more flexible and effective than conventional methods. The final depth
prediction is output by the reconstruction module. Numerous qualitative and
quantitative experiments demonstrate the effectiveness of our method, which can
generate accurate and HR depth maps, surpassing state-of-the-art methods.
Meanwhile, the rationality of modules is also proved by ablation experiments.
