RainGAN: unsupervised raindrop removal via decomposition and composition
Adherent raindrops on windshield or camera lens may distort and occlude vision, causing issues for downstream machine vision perception. Most of the existing raindrop removal methods focus on learning the mapping from a raindrop image to its clean content by training with the paired raindrop-clean i...
Main Author: | Xu, Yan |
---|---|
Other Authors: | Loke Yuan Ren |
Format: | Thesis-Master by Research |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/160029 |
Similar Items
-
Semi-RainGAN: A Semisupervised Coarse-to-Fine Guided Generative Adversarial Network for Mixture of Rain Removal
by: Rongwei Yu, et al.
Published: (2023-09-01) -
<i>Raindrop-Aware GAN</i>: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement
by: Jinah Kim, et al.
Published: (2020-10-01) -
Raindrop size distribution extracted from rain attenuation data
by: Omar AbdulKabeer, Hatem AlMokhtar
Published: (2008) -
Rain attenuation prediction based on raindrop size distribution measurement in Malaysia
by: Alhilali, Manhal Jaafar Jaber
Published: (2018) -
Rain removal using cycle-consistency adversarial network
by: Ng, Henry Siong Hock
Published: (2019)