计算机科学
降级(电信)
雪
透视图(图形)
代表(政治)
一般化
人工智能
图像(数学)
计算机视觉
人工神经网络
机器学习
模式识别(心理学)
数学
地理
电信
数学分析
政治
气象学
政治学
法学
作者
Sixiang Chen,Tian Ye,Chenghao Xue,Haoyu Chen,Yun Liu,Erkang Chen,Lei Zhu
标识
DOI:10.1145/3581783.3612003
摘要
Single-image snow removal aims to restore clean images from heterogeneous and irregular snow degradations. Recent methods utilize neural networks to remove various degradations directly. However, these approaches suffer from the limited ability to flexibly perceive complicated snow degradation patterns and insufficient representation of background structure information. To further improve the performance and generalization ability of snow removal, this paper aims to develop a novel and efficient paradigm from the perspective of degradation perceiving and background modeling.
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