17 citations · 22 across the 11 of their papers we have counts for
9 papers · 1 filter
DVANet: Degradation-aware Visual-prior Alignment Network for Image Restoration
Yanjie Tu, Qingsen Yan, Axi Niu +3
All-in-One image restoration aims to develop a unified restoration framework for handling diverse degradation types. Existing end-to-end methods usually regard the restoration proc…
GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction
Tao Hu, Weiyu Zhou, Yanjie Tu +4
Pre-trained Latent Diffusion Models (LDMs) have recently shown strong perceptual priors for low-level vision tasks, making them a promising direction for multi-exposure High Dynami…
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…
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement
Qingsen Yan, Kangbiao Shi, Yixu Feng +4
Low-Light Image Enhancement (LLIE) aims to restore vivid content and details from corrupted low-light images. However, existing standard RGB (sRGB) color space-based LLIE methods o…
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement
Kangbiao Shi, Yixu Feng, Tao Hu +5
The advent of Deep Neural Networks (DNNs) has driven remarkable progress in low-light image enhancement (LLIE), with diverse architectures (e.g., CNNs and Transformers) and color s…
Boosting HDR Image Reconstruction via Semantic Knowledge Transfer
Tao Hu, Longyao Wu, Wei Dong +5
Recovering High Dynamic Range (HDR) images from multiple Standard Dynamic Range (SDR) images become challenging when the SDR images exhibit noticeable degradation and missing conte…
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…
Generating Content for HDR Deghosting from Frequency View
Tao Hu, Qingsen Yan, Yuankai Qi +1
Recovering ghost-free High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images becomes challenging when the LDR images exhibit saturation and significant motion…
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…