Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Monte Carlo (MC) integration is used ubiquitously in realistic image synthesis because of its
flexibility and generality. However, the integration has to balance estimator bias and …

Learning a single static convolutional kernel in each convolutional layer is the common
training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent …

Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D
scenes from multi-view images. Most methods rely on simple rendering algorithms: pre …

The rising popularity of intelligent mobile devices and the daunting computational cost of
deep learning-based visual recognition models call for efficient on-device inference …

Most of the existing learning-based single image super-resolution (SISR) methods are
trained and evaluated on simulated datasets, where the low-resolution (LR) images are …

Free-viewpoint image-based rendering (IBR) is a standing challenge. IBR methods combine
warped versions of input photos to synthesize a novel view. The image quality of this …

Single image super-resolution (SISR) deals with a fundamental problem of upsampling a
low-resolution (LR) image to its high-resolution (HR) version. Last few years have witnessed …

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 …

We propose a temporally coherent generative model addressing the super-resolution
problem for fluid flows. Our work represents a first approach to synthesize four-dimensional …