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首页> 外文会议>Youth Academic Annual Conference of Chinese Association of Automation >A Novel Data and Model Hybrid-Driven Method for Image Restoration Using Residual Dense Attention U-Net
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A Novel Data and Model Hybrid-Driven Method for Image Restoration Using Residual Dense Attention U-Net

机译：一种新的数据和模型混合动力驱动方法，用于使用剩余密度关注U-Net的图像恢复

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摘要

As people’s pursuit of large screen-to-body ratio screen experience continues to improve, neither the digging front camera nor the bangs front camera can meet people’s requirements for the front camera of a mobile phone. Therefore, the research and development of full-screen equipment has become a new trend. A full-screen device requires the imaging device to be placed below the screen, which we call an under-display cameras. The under-display cameras will improve the user’s interactive experience while expanding the screen-to-body ratio of the mobile phone. However, there are many problems in the development of under-display cameras. When the imaging device is installed under the screen, the lower light transmittance will cause serious image degradation. Therefore, a new U-Net, which we call residual dense attention UNet (RDAU-Net), is proposed in this paper. A residual dense attention module which we propose in RDAU-Net to replace the single-layer convolution in the U-Net network. Meanwhile, the introduction of channel attention can effectively enhance the interdependence between channels, thereby adaptively re-dividing channel features. Experiments show that our RDAU-Net has better accuracy and faster recovery efficiency than existing methods.
机译：随着人们对大型屏幕到体比屏幕经验的追求继续提高，挖掘前相机和刘海展示既不能满足人们对手机前置摄像头的人。因此，全屏设备的研究和开发已成为一种新趋势。全屏设备要求将成像设备放在屏幕下方，我们称之为显示的相机。显示屏下的相机将在扩展手机的屏幕到体比时改善用户的交互式体验。然而，在显示屏下的开发中存在许多问题。当成像装置安装在屏幕下方时，较低的透光率会导致严重的图像劣化。因此，在本文中提出了一种新的U-Net，我们称之为剩余的密集关注（Rdau-net）。我们在RDAU-Net提出的剩余密度关注模块，以取代U-Net网络中的单层卷积。同时，引入信道注意力可以有效地增强信道之间的相互依存，从而自适应地重新分割信道特征。实验表明，我们的RDAU-Net具有比现有方法更好的准确性和更快的恢复效率。

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