6 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…
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…
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…
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…