3 papers
cs.CV2026
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…
cs.CV2025
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…
cs.CV2024
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…