Video deblurring aims to restore the latent video frames from their blurred counterparts.
Despite the remarkable progress, most promising video deblurring methods only investigate …

Video restoration concerns the recovery of a clean video sequence starting from its
degraded version. Different video restoration tasks exist, including denoising, deblurring …

Although convolutional neural networks (CNNs) have been proposed to remove adverse
weather conditions in single images using a single set of pre-trained weights, they fail to …

With frame-based cameras, capturing fast-moving scenes without suffering from blur often
comes at the cost of low SNR and low contrast. Worse still, the photometric constancy that …

Neural radiance fields (NeRF) show great success in novel-view synthesis. However, in real-
world scenes, recovering high-quality details from the source images is still challenging for …

In recent years, real image super-resolution (SR) has achieved promising results due to the
development of SR datasets and corresponding real SR methods. In contrast, the field of …

Video deblurring is a challenging task because only input blurry sequences are available.
To further constrain the optimization process, existing methods explore various additional …

Dynamic scene video deblurring aims to remove undesirable blurry artifacts captured during
the exposure process. Although previous video deblurring methods have achieved …

For video frame interpolation (VFI), existing deep-learning-based approaches strongly rely
on the ground-truth (GT) intermediate frames, which sometimes ignore the non-unique …

Accurate object tracking in low-light environments is crucial, particularly in surveillance,
ethology applications, and biometric recognition systems. However, achieving this is …