4 papers
Causal Disentanglement-Inspired Degradation Representation Learning for Full-Reference Image Quality Assessment
Zhen Zhang, Jielei Chu, Tian Zhang +5
Existing deep network-based full-reference image quality assessment (FR-IQA) models typically work by performing pairwise comparisons of deep features from the reference and distor…
NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results
Jiebin Yan, Chenyu Tu, Weixia Zhang +8
This paper presents a comprehensive review of the NITRE 2026 Efficient Low Light Image Enhancement (E-LLIE) Challenge, highlighting the proposed solutions and final outcomes. This…
Degradation-Aware Adaptive Context Gating for Unified Image Restoration
Lei He, Jielei Chu, Fengmao Lv +4
Unified image restoration using a single model often faces task interference due to diverse degradations. To address this, we propose DACG-IR (Degradation-Aware Adaptive Context Ga…
Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling
Zhihua Wang, Yu Long, Qinghua Lin +5
Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE). However, despite significant progress, their outputs may still exhibit i…