487 papers with code • 0 benchmarks • 19 datasets
Super resolution is the task of taking an input of a low resolution (LR) and upscaling it to that of a high resolution.
( Credit: MemNet )
Spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency.
In this paper, we propose a group-based bi-directional recurrent wavelet neural networks (GBR-WNN) to exploit the sequential data and spatio-temporal information effectively for VSR.
Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost.
Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement.
In this paper, we introduce a new super-resolution image reconstruction generative adversarial network framework, and a Bayesian optimization method used to optimizing the hyperparameters of the generator and discriminator.
This paper reviews the NTIRE2021 challenge on burst super-resolution.
In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement.
There is a growing demand for high-resolution (HR) medical images in both the clinical and research applications.