Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution
depth map from a low-resolution observation with the help of a paired high-resolution color …

Variational autoencoders (VAEs), as well as other generative models, have been shown to
be efficient and accurate for capturing the latent structure of vast amounts of complex high …

Deep convolutional neural networks (CNNs) have been popularly adopted in image super-
resolution (SR). However, deep CNNs for SR often suffer from the instability of training …

Spatial interpolation is a traditional geostatistical operation that aims at predicting the
attribute values of unobserved locations given a sample of data defined on point supports …

Digital elevation model (DEM) is a critical data source for variety of applications such as
road extraction, hydrological modeling, flood map**, and many geospatial studies. The …

How to improve the representational power of visual features extracted by deep
convolutional neural networks is of crucial importance for high-quality image super …

Surface detection of small defects plays a vital role in manufacturing and has attracted broad
interest. It remains challenging primarily due to the small size of the defect relative to the …

Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and obtained remarkable performance. However, most of the …

Image salient object detection (SOD) is an active research topic in computer vision and
multimedia area. Fusing complementary information of RGB and depth has been …

Depth map super-resolution is a task with high practical application requirements in the
industry. Existing color-guided depth map super-resolution methods usually necessitate an …