Segment anything model (SAM) developed by Meta AI Research has recently attracted
significant attention. Trained on a large segmentation dataset of over 1 billion masks, SAM is …

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low
light, intense glow, light scattering, and the presence of multicolored light sources. Existing …

Deep learning based automatic medical image segmentation models often suffer from
domain shift, where the models trained on a source domain do not generalize well to other …

In image restoration (IR) leveraging semantic priors from segmentation models has been a
common approach to improve performance. The recent segment anything model (SAM) has …

Introducing event cameras into video super-resolution (VSR) shows great promise. In
practice, however, integrating event data as a new modality necessitates a laborious model …

In the evolving landscape of computer vision foundation models have emerged as pivotal
tools exhibiting exceptional adaptability to a myriad of tasks. Among these the Segment …

Diffusion-based super-resolution (SR) models have recently garnered significant attention
due to their potent restoration capabilities. But conventional diffusion models perform noise …

While real-world anime super-resolution (SR) has gained increasing attention in the SR
community existing methods still adopt techniques from the photorealistic domain. In this …

Image denoising is a fundamental task in computer vision. While prevailing deep learning-
based supervised and self-supervised methods have excelled in eliminating in-distribution …

Image restoration and enhancement is a process of improving the image quality by
removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) …