.png){kind=icon}
返回
Multiscale image denoising algorithm based on UNet3+
.png){kind=icon}
PRE
AI
DOI：10.1007/s00530-024-01284-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To fully exploit the multiscale information for image denoising, we introduce the idea of full-scale skip connections in the image segmentation network UNet3+. However, existing UNet3+ networks aggregate multiscale information by directly stitching feature maps, leading to the existence of redundant information. To address this problem, we propose a multiscale selection block for feature selection across multiple convolutional streams as well as within a single scale. Specifically, the selective feature concatenation block dynamically adjusts the receptive field through a self-attentive mechanism to selectively fuse features from multiple resolutions. The dual-stream attention unit performs feature selection from both channel and spatial dimensions within each scale. Additionally, we utilize PixelShuffle for feature reconstruction to enhance multiscale semantic information and maintain information integrity. Based on the above, a novel multiscale image denoising network based on UNet3+ is proposed in this paper. Qualitative and quantitative experimental results show that our network achieves significant results in removing additive white Gaussian noise.
Keyword:
Image denoising
Multiscale
UNet plus
Convolutional neural network
期刊
IF:
3.1
论文数:
2.8K
被引数:
2.7K
机构
引用论文
Caw’s Walking State Recognition Based on Accelerometers and Gyroscopes Installed on Ear-Tags and Collar-Tags基于安装在耳标和项圈上的加速度计和陀螺仪的Caw步行状态识别
PREAI
分享收藏分享收藏Continuous Fluid Resuscitation for Treatment of Uncontrolled Hemorrhagic Shock Following Massive Splenic Injury in Rats.png)
Shock
OAAI
ShockIF0
OAAI分享收藏
分享收藏Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss使用具有Wasserstein距离和感知损失的生成对抗网络对低剂量CT图像进行去噪OAAI
OAAI分享收藏
查看更多内容