相关实验视频

Updated: Jul 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

通过使用扩展卷积的多尺度修改U-Net进行图像消除模糊

Xiao-Pei Shi1, Song-Yih Lin2, Min-Lang Yang3

  • 1School of Foreign Studies, Shaoguan University, Guangdong, China.

Science progress
|February 24, 2024
PubMed
概括

本研究介绍了一种多尺度的U-Net深度学习模型,用于消除动作模糊的交通图像的模糊. 增强的网络有效地恢复了清晰的图像,改善了城市交通系统中的车辆识别.

关键词:
这就是U-Net.盲人消除模糊的方法扩张的卷积扩张的卷积.运动模糊模糊模糊多个尺度的多个尺度.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

537
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

401

相关概念视频

Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...