44 citations · 114 across the 19 of their papers we have counts for
19 papers
Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery
Yuyan Zhou, Dong Liang, Songcan Chen +3
When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and d…
Learned Image Reasoning Prior Penetrates Deep Unfolding Network for Panchromatic and Multi-Spectral Image Fusion
Man Zhou, Jie Huang, Naishan Zheng +1
The success of deep neural networks for pan-sharpening is commonly in a form of black box, lacking transparency and interpretability. To alleviate this issue, we propose a novel mo…
Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model
Xin Jin, Jia-Wen Xiao, Ling-Hao Han +4
Explicit calibration-based methods have dominated RAW image denoising under extremely low-light environments. However, these methods are impeded by several critical limitations: a)…
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Jinghao Zhang, Feng Zhao
Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications. Nevertheless, existing works typically concentrate on regardi…
Flare7K++: Mixing Synthetic and Real Datasets for Nighttime Flare Removal and Beyond
Yuekun Dai, Chongyi Li, Shangchen Zhou +3
Artificial lights commonly leave strong lens flare artifacts on the images captured at night, degrading both the visual quality and performance of vision algorithms. Existing flare…
Adaptive Window Pruning for Efficient Local Motion Deblurring
Haoying Li, Jixin Zhao, Shangchen Zhou +3
Local motion blur commonly occurs in real-world photography due to the mixing between moving objects and stationary backgrounds during exposure. Existing image deblurring methods p…
MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results
Yuekun Dai, Chongyi Li, Shangchen Zhou +6
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobi…
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results
Qingpeng Zhu, Wenxiu Sun, Yuekun Dai +20
Depth completion from RGB images and sparse Time-of-Flight (ToF) measurements is an important problem in computer vision and robotics. While traditional methods for depth completio…
MIPI 2023 Challenge on RGBW Fusion: Methods and Results
Qianhui Sun, Qingyu Yang, Chongyi Li +7
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobi…
MIPI 2023 Challenge on RGBW Remosaic: Methods and Results
Qianhui Sun, Qingyu Yang, Chongyi Li +7
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobi…
Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera
Ruicheng Feng, Chongyi Li, Huaijin Chen +3
Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-base…
RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
Rui-Qi Wu, Zheng-Peng Duan, Chun-Le Guo +2
Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real im…
Random Weights Networks Work as Loss Prior Constraint for Image Restoration
Man Zhou, Naishan Zheng, Jie Huang +5
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our b…
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration
Man Zhou, Naishan Zheng, Jie Huang +2
Image and video restoration has achieved a remarkable leap with the advent of deep learning. The success of deep learning paradigm lies in three key components: data, model, and lo…
Iterative Prompt Learning for Unsupervised Backlit Image Enhancement
Zhexin Liang, Chongyi Li, Shangchen Zhou +2
We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-le…
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement
Chongyi Li, Chun-Le Guo, Man Zhou +4
Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. The new standard unveils many issues in existing approaches for low-l…
BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfer
Qixin Yan, Chunle Guo, Jixin Zhao +3
In this work, we propose a Robust, Efficient, and Component-specific makeup transfer method (abbreviated as BeautyREC). A unique departure from prior methods that leverage global a…
Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network
Keyu Yan, Man Zhou, Jie Huang +4
Panchromatic (PAN) and multi-spectral (MS) image fusion, named Pan-sharpening, refers to super-resolve the low-resolution (LR) multi-spectral (MS) images in the spatial domain to g…
Deep Fourier Up-Sampling
Man Zhou, Hu Yu, Jie Huang +5
Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling. However, spatial up-sampling operators (\emph{e.g.}, interpolation, transpos…