93 citations · 145 across the 8 of their papers we have counts for
8 papers
Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions
Yu Zhang, Xiaoguang Di, Junde Wu +6
Image enhancement is a common technique used to mitigate issues such as severe noise, low brightness, low contrast, and color deviation in low-light images. However, providing an o…
Self-supervised Low Light Image Enhancement and Denoising
Yu Zhang, Xiaoguang Di, Bin Zhang +3
This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the…
Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks
Yu Zhang, Xiaoguang Di, Bin Zhang +2
Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal wi…
Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning
Junde Wu, Shuang Yu, Wenting Chen +5
Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts. However, a well trained deep learning model deman…
Learning an Adaptive Model for Extreme Low-light Raw Image Processing
Qingxu Fu, Xiaoguang Di, Yu Zhang
Low-light images suffer from severe noise and low illumination. Current deep learning models that are trained with real-world images have excellent noise reduction, but a ratio par…
TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks
Xinyu Liu, Xiaoguang Di
Lightweight or mobile neural networks used for real-time computer vision tasks contain fewer parameters than normal networks, which lead to a constrained performance. In this work,…
Self-supervised Image Enhancement Network: Training with Low Light Images Only
Yu Zhang, Xiaoguang Di, Bin Zhang +1
This paper proposes a self-supervised low light image enhancement method based on deep learning. Inspired by information entropy theory and Retinex model, we proposed a maximum ent…
Integrating neural networks into the blind deblurring framework to compete with the end-to-end learning-based methods
Junde Wu, Xiaoguang Di, Jiehao Huang +1
Recently, end-to-end learning-based methods based on deep neural network (DNN) have been proven effective for blind deblurring. Without human-made assumptions and numerical algorit…