activity
20232026
most citedYou Only Need One Color Space: An Efficient Network for Low-light Image Enhancement

18 citations · 44 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2026

DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration

Zhaokun He, Kangbiao Shi, Axi Niu +4

All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model. However, existing methods often overlook image semantics in degradation modeling an…

cs.CV2026

Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation

Minyan Luo, Yuxin Zhang, Yifei Li +5

Narrative-driven product photography has become a prevalent paradigm in modern marketing, as coherent visual storytelling helps convey product value and establishes emotional engag…

cs.CV2026

UniBlendNet: Unified Global, Multi-Scale, and Region-Adaptive Modeling for Ambient Lighting Normalization

Jiatao Dai, Wei Dong, Han Zhou +2

Ambient Lighting Normalization (ALN) aims to restore images degraded by complex, spatially varying illumination conditions. Existing methods, such as IFBlend, leverage frequency-do…

cs.CV2026

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)

Guanyi Qin, Jie Liang, Bingbing Zhang +50

In this paper, we present an overview of the NTIRE 2026 challenge on the 3rd Restore Any Image Model in the Wild, specifically focusing on Track 1: Professional Image Quality Asses…

cs.CV2026

PaAgent: Portrait-Aware Image Restoration Agent via Subjective-Objective Reinforcement Learning

Yijian Wang, Qingsen Yan, Jiantao Zhou +2

Image Restoration (IR) agents, leveraging multimodal large language models to perceive degradation and invoke restoration tools, have shown promise in automating IR tasks. However,…

cs.CV2025★ 2 cited

HVI: A New Color Space for Low-light Image Enhancement

Qingsen Yan, Yixu Feng, Cheng Zhang +6

Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods ar…

eess.IV2024★ 2 cited

Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition

Genggeng Chen, Kexin Dai, Kangzhen Yang +7

In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear content photos is challenging. While significant progress has been made in synthesizin…

cs.CV2024★ 3 cited

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu, Zongwei Wu, Ao Li +109

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 netw…

cs.CV2024★ 2 cited

CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task

Kangzhen Yang, Tao Hu, Kexin Dai +6

In real-world scenarios, images captured often suffer from blurring, noise, and other forms of image degradation, and due to sensor limitations, people usually can only obtain low…

cs.CV2024★ 18 cited

You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement

Qingsen Yan, Yixu Feng, Cheng Zhang +5

Low-Light Image Enhancement (LLIE) task tends to restore the details and visual information from corrupted low-light images. Most existing methods learn the mapping function betwee…

cs.CV2023★ 17 cited

Towards High-quality HDR Deghosting with Conditional Diffusion Models

Qingsen Yan, Tao Hu, Yuan Sun +5

High Dynamic Range (HDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. Despite the remarkable progress, DN…