Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2104.06977
