34 citations · 66 across the 6 of their papers we have counts for
7 papers
Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising
Hansen Feng, Lizhi Wang, Yiqi Huang +3
Recently, the mainstream practice for training low-light raw image denoising methods has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing t…
Recurrent Spike-based Image Restoration under General Illumination
Lin Zhu, Yunlong Zheng, Mengyue Geng +2
Spike camera is a new type of bio-inspired vision sensor that records light intensity in the form of a spike array with high temporal resolution (20,000 Hz). This new paradigm of v…
Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
Tong Li, Hansen Feng, Lizhi Wang +2
Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding. Current methods either struggle with…
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling
Hansen Feng, Lizhi Wang, Yuzhi Wang +1
Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream. However, the limit…
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li, Kai Zhang, Radu Timofte +108
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…
Image-specific Convolutional Kernel Modulation for Single Image Super-resolution
Yuanfei Huang, Jie Li, Yanting Hu +2
Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samp…
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution
Yuanfei Huang, Jie Li, Yanting Hu +2
Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally…