Convolutions are the fundamental building blocks of CNNs. The fact that their weights are
spatially shared is one of the main reasons for their widespread use, but it is also a major …

Dense depth perception is critical for autonomous driving and other robotics applications.
However, modern LiDAR sensors only provide sparse depth measurement. It is thus …

Convolution is one of the basic building blocks of CNN architectures. Despite its common
use, standard convolution has two main shortcomings: Content-agnostic and Computation …

Image processing and pixel-wise dense prediction have been advanced by harnessing the
capabilities of deep learning. One central issue of deep learning is the limited capacity to …

During the past years, tremendous advances in image restoration tasks have been achieved
using highly complex neural networks. Despite their good restoration performance, the …

Joint image filters can leverage the guidance image as a prior and transfer the structural
details from the guidance image to the target image for suppressing noise or enhancing …

Abstract Analysis sparse representation (ASR) and synthesis sparse representation (SSR)
are two representative approaches for sparsity-based image modeling. An image is …

Rapid development of affordable and portable consumer depth cameras facilitates the use
of depth information in many computer vision tasks such as intelligent vehicles and 3D …

When smartphone cameras are used to take photos of digital screens, usually moiré
patterns result, severely degrading photo quality. In this paper, we design a wavelet-based …

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by
enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of …