.png){kind=icon}
返回
Deep blur detection network with boundary-aware multi-scale features
.png){kind=icon}
OA
AI
DOI：10.1080/09540091.2021.1933906.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, blur detection is a hot topic in computer vision. It can accurately segment the blurred areas from an image, which is conducive for the post-processing of the image. Although many hand-crafted features based approaches have been presented during the last decades, they were not robust to the complex scenarios. To solve this problem, we newly establish a boundary-aware multi-scale deep network in this paper. First, the VGG-16 network is used to extract the deep features from multi-scale layers. Contrast layers and deconvolutional layers are added to make the difference between the blurred areas and clear areas more prominent. At last, a new boundary-aware penalty is introduced, which makes the edges of our results much clearer. Our method spends about 0.2 s to evaluate an image. Experiments on the large dataset confirm that the proposed model performs better than other models.
Keyword:
Blur detection
multi-scale
boundary-aware
deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
854
被引数:
1.5K
机构
引用论文
Refocusing and Motion Parameter Estimation for Ground Moving Targets Based on Improved Axis Rotation-Time Reversal Transform基于改进轴旋转-时间反转变换的地面运动目标重聚焦与运动参数估计
PREAI
分享收藏The Psychological Effects and Benefits of Using Green Spaces in the City: A Field Experiment with Young Polish Adults.png)
Forests
OAAI
ForestsIF0
OAAI分享收藏Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive thresholdCONNECTION SCIENCE
CONNECTION SCIENCEIF3.4
PREAI
分享收藏
查看更多内容