Recently, the first foundation model developed specifically for image segmentation tasks
was developed, termed the" Segment Anything Model"(SAM). SAM can segment objects in …

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 …

Image deblurring is a critical task in the field of image restoration, aiming to eliminate
blurring artifacts. However, the challenge of addressing non-uniform blurring leads to an ill …

Abstract Segment Anything Model (SAM) has emerged as a transformative approach in
image segmentation acclaimed for its robust zero-shot segmentation capabilities and …

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) …

In overcoming the challenges faced in adapting to paired real-world data, recent
unsupervised single image deraining (SID) methods have proven capable of accomplishing …

Recently, Segment Anything Model (SAM) shows exceptional performance in generating
high-quality object masks and achieving zero-shot image segmentation. However, as a …

In this study, we address the intricate challenge of multi-task dense prediction,
encompassing tasks such as semantic segmentation, depth estimation, and surface normal …

Low-light images often suffer from severe detail lost in darker areas and non-uniform
illumination distribution across distinct regions. Thus, structure modeling and region-specific …