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一个轻量级的混合感知增强网络,用于红外图像超分辨率
Zepeng Liu1, Jiya Tian2, Chao Liu2
1Xinjiang Institute of Technology, School of Information Engineering, Aksu, 843000, China. smilenorth089@163.com.
Scientific reports
|January 29, 2026
概括
这项研究引入了一种新的混合感知增强网络 (HPEN),用于红外图像超分辨率 (SR). 与现有方法相比,HPEN提供了更高的性能和效率,使其成为增强低分辨率红外图像的宝贵工具.
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:
- 由于当前的卷积神经网络 (CNN) 和变压器模型的局限性,红外图像超分辨率 (SR) 是一个挑战.
- 美国有线电视电视与远程依赖性作斗争,而变形金刚是计算密集型,错过了局部细节.
研究的目的:
- 开发一种新的混合感知增强网络 (HPEN),以提高红外图像超分辨率.
- 通过结合全球上下文建模和局部细节提取来解决现有的SR方法的局限性.
主要方法:
- 提出了一个混合感知增强网络 (HPEN),其中包括一个混合感知增强块 (HPEB).
- HPEB集成了一个用于全球背景的代币聚合区块 (TAB),一个用于本地细节的多尺度特征增强区块 (MFEB),以及用于改进的卷积层.
主要成果:
- 在红外图像超分辨率任务中,HPEN实现了领先的性能,超过了现有的方法.
- 在轻量级方法中实现了[公式:参见文本]SR任务的最佳峰值信号噪声比 (PSNR) 和结构相似度指数 (SSIM) 测量.
- 与HiT-SR相比,显示出显著的效率优势,减少了42.9%的FLOP和90.6%的GPU内存使用量,并具有更快的推断速度.
结论:
- 拟议的HPEN有效地模拟了高级红外SR的全球背景和本地细节.
- 惠普 (HPEN) 为红外图像超分辨率提供了一种计算效率高和高性能解决方案.
- 开发的网络为红外成像的轻量级SR方法提供了重大进步.
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