Motion blur is a common photography artifact in dynamic environments that typically comes
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature
Fusion based on the U-Net architecture. The proposed method is designed based on two …

We present a new end-to-end generative adversarial network (GAN) for single image motion
deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring …

Amphibious ground-aerial vehicles fuse flying and driving modes to enable more flexible air-
land mobility and have received growing attention recently. By analyzing the existing …

While deep neural networks (DNN) based single image super-resolution (SISR) methods
are rapidly gaining popularity, they are mainly designed for the widely-used bicubic …

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision
over the past five years. According to the nature of different applications, designing …

We present a joint learning scheme of video super-resolution and deblurring called VSRDB
to restore clean high-resolution (HR) videos from blurry low-resolution (LR) ones. This joint …

Existing deep learning approaches to single image super-resolution have achieved
impressive results but mostly assume a setting with fixed pairs of high resolution and low …

Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem
due to the joint degradation of motion blurs and low spatial resolution. In this article, we …