Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a
defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus …

Effectively encoding multi-scale contextual information is crucial for accurate semantic
segmentation. Most of the existing transformer-based segmentation models combine …

Motivated by their great potential to reduce the size, cost, and weight, flat lenses, a category
that includes diffractive lenses and metalenses, are rapidly emerging as key components …

Previous fully-supervised defocus deblurring has made significant progress. However,
training such deep models requires abundant paired ground truth, which is expensive and …

Restoring image under multiple weather conditions in an all-in-one fashion remains a
formidable challenge due to images captured under different weather conditions exhibit …

This paper introduces ConStyle v2, a strong plug-and-play prompter designed to output
clean visual prompts and assist U-Net Image Restoration models in handling multiple …

Abstract Neural Radiance Fields (NeRF) has gained prominence in the domain of 3D
reconstruction. Despite its popularity, NeRF algorithm typically require clear, static images to …

In recent years, with the rapid developments in deep learning and graphics processing units,
learning-based defocus deblurring has made favorable achievements. However, the current …

Error concealment is an important technique to restore a damaged video bistream. Although
data-driven in-painting methods can be directly applied to video error concealment, existing …

Transformers in image deblurring have achieved significant progress recently under their
outstanding ability to model the long-range information of features. However, the modeling …