Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve
the resolution of images or videos in computer vision. In recent years, significant progress …

This work addresses the problem of semantic foggy scene understanding (SFSU). Although
extensive research has been performed on image dehazing and on semantic scene …

Convolutional neural networks have recently demonstrated high-quality reconstruction for
single image super-resolution. However, existing methods often require a large number of …

Rain streaks, particularly in heavy rain, not only degrade visibility but also make many
computer vision algorithms fail to function properly. In this paper, we address this visibility …

Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR)
input image is challenging due to missing details in under-/over-exposed regions caused by …

Numerous single image blind deblurring algorithms have been proposed to restore latent
sharp images under camera motion. However, these algorithms are mainly evaluated using …

Obtaining a high quality high dynamic range (HDR) image in the presence of camera and
object movement has been a long‐standing challenge. Many methods, known as HDR …

Generic object level saliency detection is important for many vision tasks. Previous
approaches are mostly built on the prior that “appearance contrast between objects and …

Intrinsic image decomposition separates an image into a reflectance layer and a shading
layer. Automatic intrinsic image decomposition remains a significant challenge, particularly …

To detect visually salient elements of complex natural scenes, computational bottom-up
saliency models commonly examine several feature channels such as color and orientation …