Poster "image denoising" Papers
15 papers found
Conference
Can Diffusion Models Disentangle? A Theoretical Perspective
Liming Wang, Muhammad Jehanzeb Mirza, Yishu Gong et al.
NEURIPS 2025arXiv:2504.00220
Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising
Yuchen Wang, Hongyuan Wang, Lizhi Wang et al.
CVPR 2025arXiv:2412.16645
5
citations
Diffusion Image Prior
Hamadi Chihaoui, Paolo Favaro
ICCV 2025arXiv:2503.21410
2
citations
From Softmax to Score: Transformers Can Effectively Implement In-Context Denoising Steps
Paul Rosu, Lawrence Carin, Xiang Cheng
NEURIPS 2025
IDF: Iterative Dynamic Filtering Networks for Generalizable Image Denoising
Dongjin Kim, Jaekyun Ko, Muhammad Kashif Ali et al.
ICCV 2025arXiv:2508.19649
4
citations
Learning to See in the Extremely Dark
Hai Jiang, Binhao Guan, Zhen Liu et al.
ICCV 2025arXiv:2506.21132
1
citations
Random Is All You Need: Random Noise Injection on Feature Statistics for Generalizable Deep Image Denoising
Zhengwei Yin, Hongjun Wang, Guixu Lin et al.
ICLR 2025
3
citations
Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising
Sébastien Herbreteau, Michael Unser
ICCV 2025arXiv:2407.17399
4
citations
DiffBIR: Toward Blind Image Restoration with Generative Diffusion Prior
Xinqi Lin, Jingwen He, Ziyan Chen et al.
ECCV 2024arXiv:2308.15070
283
citations
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
Sayanton Vhaduri Dibbo, Adam Breuer, Juston Moore et al.
ECCV 2024arXiv:2403.14772
7
citations
Is Kernel Prediction More Powerful than Gating in Convolutional Neural Networks?
Lorenz K. Muller
ICML 2024
LAN: Learning to Adapt Noise for Image Denoising
Changjin Kim, Tae Hyun Kim, Sungyong Baik
CVPR 2024arXiv:2412.10651
21
citations
Learning Pseudo-Contractive Denoisers for Inverse Problems
Deliang Wei, Peng Chen, Fang Li
ICML 2024arXiv:2402.05637
7
citations
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
Dihan Zheng, Yihang Zou, Xiaowen Zhang et al.
CVPR 2024arXiv:2403.17502
4
citations
TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts
Youssef Mansour, Xuyang Zhong, Serdar Caglar et al.
ECCV 2024
8
citations