collaborators

5 papers

cs.CV2026

Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices

Guangrui Bai, Hailong Yan, Wenhai Liu +2

Real-time low-light image enhancement on mobile and embedded devices requires models that balance visual quality and computational efficiency. Existing deep learning methods often…

cs.CV2026

AnimeAgent: Is the Multi-Agent via Image-to-Video models a Good Disney Storytelling Artist?

Hailong Yan, Shice Liu, Tao Wang +5

Custom Storyboard Generation (CSG) aims to produce high-quality, multi-character consistent storytelling. Current approaches based on static diffusion models, whether used in a one…

cs.CV2026

Revisiting Lightweight Low-Light Image Enhancement: From a YUV Color Space Perspective

Hailong Yan, Shice Liu, Xiangtao Zhang +4

In the current era of mobile internet, Lightweight Low-Light Image Enhancement (L3IE) is critical for mobile devices, which faces a persistent trade-off between visual quality and…

cs.CV2025

NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu +102

This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of…

cs.CV2025

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices

Hailong Yan, Ao Li, Xiangtao Zhang +4

Recent advancements in deep neural networks have driven significant progress in image enhancement (IE). However, deploying deep learning models on resource-constrained platforms, s…