35 citations · 96 across the 13 of their papers we have counts for
13 papers
Poisson2Gaussian: Noise Gaussianization to Enhance Image Denoising
Xirou Zhou, Zijing Xu, Yibo Qu +3
The quantum nature of light determines the inherent Poisson stochasticity of photon detection, which is ubiquitous in photography, microscopy, and astronomy. However, our controlle…
Self-supervised Dynamic Heterogeneous Degradation Modeling for Unified Zero-Shot Image Restoration
XiaoWan Hu, Jing Yang, HeNan Liu +2
Zero-shot image restoration provides a flexible way to handle diverse degradations without task-specific training. However, existing methods typically rely on stacked layers or pre…
Measuring and Controlling the Spectral Bias for Self-Supervised Image Denoising
Wang Zhang, Huaqiu Li, Xiaowan Hu +3
Current self-supervised denoising methods for paired noisy images typically involve mapping one noisy image through the network to the other noisy image. However, after measuring t…
Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios
Huaqiu Li, Xiaowan Hu, Haoqian Wang
Real-world low-light images often suffer from complex degradations such as local overexposure, low brightness, noise, and uneven illumination. Supervised methods tend to overfit to…
Prompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single-Image Denoising
Huaqiu Li, Wang Zhang, Xiaowan Hu +3
Many studies have concentrated on constructing supervised models utilizing paired datasets for image denoising, which proves to be expensive and time-consuming. Current self-superv…
Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video Denoising
Zikang Chen, Tao Jiang, Xiaowan Hu +3
Self-supervised video denoising aims to remove noise from videos without relying on ground truth data, leveraging the video itself to recover clean frames. Existing methods often r…
Spatiotemporal Graph Guided Multi-modal Network for Livestreaming Product Retrieval
Xiaowan Hu, Yiyi Chen, Yan Li +5
With the rapid expansion of e-commerce, more consumers have become accustomed to making purchases via livestreaming. Accurately identifying the products being sold by salespeople,…
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
Jing Lin, Xiaowan Hu, Yuanhao Cai +5
How to properly model the inter-frame relation within the video sequence is an important but unsolved challenge for video restoration (VR). In this work, we propose an unsupervised…
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
Yuanhao Cai, Xiaowan Hu, Haoqian Wang +3
Existing deep learning real denoising methods require a large amount of noisy-clean image pairs for supervision. Nonetheless, capturing a real noisy-clean dataset is an unacceptabl…
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Yuanhao Cai, Jing Lin, Xiaowan Hu +5
Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i.e., recovering the 3D hyperspectral images (HSIs) from a 2D…
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
Xiaowan Hu, Yuanhao Cai, Jing Lin +5
The rapid development of deep learning provides a better solution for the end-to-end reconstruction of hyperspectral image (HSI). However, existing learning-based methods have two…
Flow-Guided Sparse Transformer for Video Deblurring
Jing Lin, Yuanhao Cai, Xiaowan Hu +7
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring. However, CNN-based methods show limitations in capturing long-range…
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
Yuanhao Cai, Jing Lin, Xiaowan Hu +5
Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The HS…