140 citations · 181 across the 6 of their papers we have counts for
6 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…
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
Junde Wu, Wei Ji, Huazhu Fu +3
The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of computer vision, thanks to its image generation applications, such as Imagen, Latent Diffusio…
ExpNet: A unified network for Expert-Level Classification
Junde Wu, Huihui Fang, Yehui Yang +4
Different from the general visual classification, some classification tasks are more challenging as they need the professional categories of the images. In the paper, we call them…
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Junde Wu, Rao Fu, Huihui Fang +5
Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stabl…
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…
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…