3 citations · 4 across the 2 of their papers we have counts for
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cs.CV2023
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…
cs.CV2022★ 1 cited
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…
cs.CV2019
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…