This paper reviews the NTIRE 2024 low light image enhancement challenge highlighting the
proposed solutions and results. The aim of this challenge is to discover an effective network …

In the era of the Internet and the Internet of Things, display technology has evolved
significantly toward full‐scene display and realistic display. Incorporating “intelligence” into …

In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various
tasks with only a handful of prompts and examples. But in computer vision, the difficulties for …

Despite the remarkable achievement of recent underwater image restoration techniques, the
lack of labeled data has become a major hurdle for further progress. In this work, we …

Low-light image enhancement (LLIE) investigates how to improve illumination and produce
normal-light images. The majority of existing methods improve low-light images via a global …

This paper studies a diffusion-based framework to address the low-light image
enhancement problem. To harness the capabilities of diffusion models, we delve into this …

Learning to leverage the relationship among diverse image restoration tasks is quite
beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have …

Despite substantial progress all-in-one image restoration (IR) grapples with persistent
challenges in handling intricate real-world degradations. This paper introduces MPerceiver …

Universal image restoration is a practical and potential computer vision task for real-world
applications. The main challenge of this task is handling the different degradation …

We propose residual denoising diffusion models (RDDM) a novel dual diffusion process that
decouples the traditional single denoising diffusion process into residual diffusion and noise …