Motion deblurring is one of the fundamental problems of computer vision and has received
continuous attention. The variability in blur, both within and across images, imposes …

Motion deblurring is one of the fundamental problems of computer vision and has received
continuous attention. The variability in blur, both within and across images, imposes …

All-in-one image restoration has recently developed to be a new research trend in the low-
level computer vision field, aiming to tackle multiple image degradation types …

Recently, the biological perception has been a powerful tool for handling the camouflaged
object detection (COD) task. However, most existing methods are heavily dependent on the …

Defocus deblurring, especially when facing spatially varying blur due to scene depth,
remains a challenging problem. While recent advancements in network architectures have …

The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference
into a given image/video. Existing methods usually focus on pursuing the balance between …

Lens flare is a common image artifact that can significantly degrade image quality and affect
the performance of computer vision systems due to a strong light source pointing at the …

Recovering a sequence of latent sharp frames from a motion-blurred image is a challenging
task. The bio-inspired event camera, which produces an event stream with high temporal …

In this research, we introduce MaeFuse, a novel autoencoder model designed for infrared
and visible image fusion (IVIF). The existing approaches for image fusion often rely on …

Defocus deblurring using dual-pixel sensors has gathered significant attention in recent
years. However, current methodologies have not adequately addressed the challenge of …