基于交叉级特征融合的红外夜视图增强算法
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
PloS one
|September 4, 2025
概括
这项研究引入了一种新的红外夜视图像增强算法,使用跨级特征融合. 该方法有效降低噪音并提高图像质量,实现高PSNR和SSIM值.
科学领域:
- 计算机视觉
- 图像处理
- 人工智能
背景情况:
- 由于光线不足,红外夜视图像质量不佳,导致颜色溢出和不连续性.
- 单特征融合方法通常会在增强后产生更大的光环区域和更低的峰值信号噪声比 (PSNR).
- 现有的增强技术在低光条件下难以有效解决噪音和增强视觉细节.
研究的目的:
- 提出一种新的红外夜视图像增强算法.
- 解决现有的处理噪音和改善视觉质量的方法的局限性.
- 通过先进的功能融合实现红外夜视图像的高质量增强.
主要方法:
- 使用光滑波形分解和基于邻近的波形系数缩小算法去除红外图像.
- 通过Retinex算法进行初步图像增强,用于光度估计的双边过,以及用于反射增强的Sigmoid功能.
- 构建一个跨层次的特征融合网络,用于多层次的特征提取,重建和自适应融合,并优化了关节损失功能.
主要成果:
- 建议的算法有效地减少红外图像中的噪声干扰.
- 初步增强通过解决照度和反射来改善整体视觉效果.
- 跨层次的功能融合网络进一步增强功能信息,从而产生高质量的图像输出.
- 实验结果显示PSNR值超过30dB,结构相似度指数 (SSIM) 值超过0.73.
结论:
- 开发的算法显著增强了红外夜视图像.
- 跨层次的功能融合方法在改善图像质量和视觉感知方面是有效的.
- 与现有技术相比,该方法实现了更高的性能和更高的增强效果.
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