HPCDNet:混合位置编码和双频域转换网络,用于低光图像增强
Mingju Chen1,2, Hongyang Li1, Hongming Peng1
1School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
本研究介绍了HPCDNet,HPCDNet是一个新的深度学习网络,用于低光图像增强. 它有效地利用位置和频率域信息来改善图像可见性,对比度和细节保存.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 低光图像增强 (LLIE) 旨在恢复在低光照明下拍摄的图像的可见性.
- 现有的LLIE方法往往无法充分利用空间位置和频域信息.
- 这限制了他们准确重建细节和提高视觉质量的能力.
研究的目的:
- 提出一个端到端的网络,HPCDNet,用于有效的低光图像增强.
- 将混合位置编码和频域恢复集成到一个统一的框架中.
- 为了提高增强低光图像的视觉质量,细节保存和纹理保真.
主要方法:
- 在其自我注意机制中,HPCDNet采用混合位置编码技术来保留空间信息.
- 离散波纹和共弦变换用于恢复丢失的频域信息.
- 一个双重注意模块可自适应地权衡和合并恢复的频域特征.
主要成果:
- 拟议的HPCDNet显著提高了低光图像的可见性,对比度和色彩特性.
- 与现有技术相比,该方法表明图像细节和纹理的保存优越.
- 实验结果验证了整合位置和频域信息的有效性.
结论:
- 通过有效地利用空间和频率域特征,HPCDNet提供了用于低光图像增强的先进解决方案.
- 该网络的混合定位编码和双重注意模块有助于改进图像重建.
- 这种方法推进了低亮度图像增强的最新技术,产生了更自然和视觉上更有吸引力的结果.
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