5 citations · 8 across the 3 of their papers we have counts for
4 papers
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement
Xinxu Wei, Xianshi Zhang, Shisen Wang +2
Low-light image enhancement is a challenging low-level computer vision task because after we enhance the brightness of the image, we have to deal with amplified noise, color distor…
DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement
Xinxu Wei, Xianshi Zhang, Shisen Wang +4
Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color distortion, unknown nois…
BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration
Xinxu Wei, Xianshi Zhang, Shisen Wang +4
Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color bias, unknown noise, det…
Thermal Infrared Image Colorization for Nighttime Driving Scenes with Top-Down Guided Attention
Fuya Luo, Yunhan Li, Guang Zeng +3
Benefitting from insensitivity to light and high penetration of foggy environments, infrared cameras are widely used for sensing in nighttime traffic scenes. However, the low contr…