262 citations · 1.7k across the 63 of their papers we have counts for
15 papers · 1 filter
ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal
Lanqing Guo, Chong Wang, Wenhan Yang +4
Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the…
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
Yeying Jin, Ruoteng Li, Wenhan Yang +1
Estimating the reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often re…
DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity
Yeying Jin, Wei Ye, Wenhan Yang +2
Removing soft and self shadows that lack clear boundaries from a single image is still challenging. Self shadows are shadows that are cast on the object itself. Most existing metho…
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
Yeying Jin, Wending Yan, Wenhan Yang +1
Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or…
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues
Zitong Yu, Rizhao Cai, Zhi Li +3
Face anti-spoofing (FAS) and face forgery detection play vital roles in securing face biometric systems from presentation attacks (PAs) and vicious digital manipulation (e.g., deep…
Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning
Shixing Yu, Yiyang Ma, Wenhan Yang +2
Existing video frame interpolation methods can only interpolate the frame at a given intermediate time-step, e.g. 1/2. In this paper, we aim to explore a more generalized kind of v…
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement
Zhangkai Ni, Wenhan Yang, Hanli Wang +3
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of…
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression
Yeying Jin, Wenhan Yang, Robby T. Tan
Night images suffer not only from low light, but also from uneven distributions of light. Most existing night visibility enhancement methods focus mainly on enhancing low-light reg…
Feature-Aligned Video Raindrop Removal with Temporal Constraints
Wending Yan, Lu Xu, Wenhan Yang +1
Existing adherent raindrop removal methods focus on the detection of the raindrop locations, and then use inpainting techniques or generative networks to recover the background beh…
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
Yongzhen Wang, Xuefeng Yan, Fu Lee Wang +4
While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known d…
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
Yiyang Shen, Mingqiang Wei, Sen Deng +5
The intricacy of rainy image contents often leads cutting-edge deraining models to image degradation including remnant rain, wrongly-removed details, and distorted appearance. Such…
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Yi Yu, Wenhan Yang, Yap-Peng Tan +1
Rain removal aims to remove rain streaks from images/videos and reduce the disruptive effects caused by rain. It not only enhances image/video visibility but also allows many compu…
Neural Data-Dependent Transform for Learned Image Compression
Dezhao Wang, Wenhan Yang, Yueyu Hu +1
Learned image compression has achieved great success due to its excellent modeling capacity, but seldom further considers the Rate-Distortion Optimization (RDO) of each input image…
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
Danni Huang, Lingyu Zhu, Zihao Lin +3
There is an increasing consensus that the design and optimization of low light image enhancement methods need to be fully driven by perceptual quality. With numerous approaches pro…
Enhancing Low-Light Images in Real World via Cross-Image Disentanglement
Lanqing Guo, Renjie Wan, Wenhan Yang +2
Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e.g., real noise. Existing supervised enlightening algorithms require a large s…