1 citations · 4 across the 17 of their papers we have counts for
17 papers
EvoGS: Modeling Deformation Evolution for Dynamic Gaussian Splatting
Wei Dong, Shahram Shirani, Jun Chen +1
Recent extensions of 3D Gaussian Splatting (3DGS) enable real-time novel view synthesis in dynamic scenes by learning time-conditioned Gaussian deformations. However, existing MLP-…
Rethinking Exposure Correction for Spatially Non-uniform Degradation
Ao Li, Jiawei Sun, Le Dong +2
Real-world exposure correction is fundamentally challenged by spatially non-uniform degradations, where diverse exposure errors frequently coexist within a single image. However, e…
HistoFusionNet: Histogram-Guided Fusion and Frequency-Adaptive Refinement for Nighttime Image Dehazing
Mohammad Heydari, Wei Dong, Shahram Shirani +2
Nighttime image dehazing remains a challenging low-level vision problem due to the joint presence of haze, glow, non-uniform illumination, color distortion, and sensor noise, which…
SemiNFT: Learning to Transfer Presets from Imitation to Appreciation via Hybrid-Sample Reinforcement Learning
Melany Yang, Yuhang Yu, Diwang Weng +2
Photorealistic color retouching plays a vital role in visual content creation, yet manual retouching remains inaccessible to non-experts due to its reliance on specialized expertis…
Sharp Monocular View Synthesis in Less Than a Second
Lars Mescheder, Wei Dong, Shiwei Li +10
We present SHARP, an approach to photorealistic view synthesis from a single image. Given a single photograph, SHARP regresses the parameters of a 3D Gaussian representation of the…
Zero-Reference Joint Low-Light Enhancement and Deblurring via Visual Autoregressive Modeling with VLM-Derived Modulation
Wei Dong, Han Zhou, Junwei Lin +1
Real-world dark images commonly exhibit not only low visibility and contrast but also complex noise and blur, posing significant restoration challenges. Existing methods often rely…
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…
AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content
Shushi Wang, Chunyi Li, Zicheng Zhang +5
AI-based image enhancement techniques have been widely adopted in various visual applications, significantly improving the perceptual quality of user-generated content (UGC). Howev…
MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior
Liangyan Li, Yimo Ning, Kevin Le +4
This paper introduces a novel framework for image and video demoiréing by integrating Maximum A Posteriori (MAP) estimation with advanced deep learning techniques. Demoiréing addre…
Retinex-guided Histogram Transformer for Mask-free Shadow Removal
Wei Dong, Han Zhou, Seyed Amirreza Mousavi +1
While deep learning methods have achieved notable progress in shadow removal, many existing approaches rely on shadow masks that are difficult to obtain, limiting their generalizat…
Towards Scale-Aware Low-Light Enhancement via Structure-Guided Transformer Design
Wei Dong, Yan Min, Han Zhou +1
Current Low-light Image Enhancement (LLIE) techniques predominantly rely on either direct Low-Light (LL) to Normal-Light (NL) mappings or guidance from semantic features or illumin…
LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors
Han Zhou, Wei Dong, Jun Chen
Directly employing 3D Gaussian Splatting (3DGS) on images with adverse illumination conditions exhibits considerable difficulty in achieving high-quality, normally-exposed represen…
Low-Light Image Enhancement via Generative Perceptual Priors
Han Zhou, Wei Dong, Xiaohong Liu +3
Although significant progress has been made in enhancing visibility, retrieving texture details, and mitigating noise in Low-Light (LL) images, the challenge persists in applying c…
ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction
Wei Dong, Han Zhou, Yulun Zhang +2
Exposure Correction (EC) aims to recover proper exposure conditions for images captured under over-exposure or under-exposure scenarios. While existing deep learning models have sh…
GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval
Han Zhou, Wei Dong, Xiaohong Liu +4
Most existing Low-light Image Enhancement (LLIE) methods either directly map Low-Light (LL) to Normal-Light (NL) images or use semantic or illumination maps as guides. However, the…
ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer
Wei Dong, Han Zhou, Yuqiong Tian +4
Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination, consequently degrading various vision applications including object detection and s…
DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer
Wei Dong, Han Zhou, Ruiyi Wang +3
Image dehazing, a pivotal task in low-level vision, aims to restore the visibility and detail from hazy images. Many deep learning methods with powerful representation learning cap…