Infrared Image Non-uniformity Correction Algorithm Based on Full Convolutional Network
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摘要: 针对红外成像系统在经过两点校正后,随时间漂移仍然会出现的非均匀性噪声,提出一种基于全卷积深度学习网络的红外图像非均匀性校正算法,使用子网络与主网络相结合的方式进行非均匀性校正。该算法设计了非均匀性等级估计子网络,将含有非均匀性噪声的红外图像输入子网络后,输出非均匀性等级估计图,并和待校正红外图像一并输入校正主网络。子网络生成的非均匀性等级估计图作为一个参数输入校正主网络,避免了网络只针对同一等级非均匀性产生过拟合。经过实验验证,该算法克服了传统的基于场景的算法所产生的边缘模糊问题,对条纹状非均匀性噪声校正效果较好,经过校正后的红外图像清晰度高、细节丰富、边缘清晰、图像质量良好。
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关键词:
- 非均匀性等级估计子网络 /
- 红外图像 /
- 非均匀性校正 /
- 深度学习
Abstract: The infrared imaging system will still exhibit non-uniform noise after two-point correction. An infrared image non-uniformity correction algorithm based on a fully convolutional deep learning network was proposed in response to this problem. This algorithm combines the subnetwork and main network for non-uniformity correction. The network contains a nonuniformity-level estimation subnetwork. After inputting the infrared image with non-uniform noise into the non-uniformity level estimation subnetwork, the outputted non-uniformity level estimation map is input into the main network together with the original noise infrared image. The non-uniformity level estimated map generated by the subnetwork prevents the network from overfitting only for the non-uniformity of the same grade. After experimental verification, the algorithm overcomes the problem of edge blur generated by the scene-based algorithm. The algorithm will not appear blurred, the images have high definition and rich details, and the quality of images is good. -
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表 1 子网络参数
Table 1 Parameters of the subnetwork
Layer Filters Input Output Conv 3×3×32 256×256×1 256×256×32 Conv 3×3×32 256×256×32 256×256×32 Conv 3×3×32 256×256×32 256×256×32 Conv 3×3×1 256×256×32 256×256×1 表 2 校正主网络参数
Table 2 Parameters of main network
Layer Filters Input Output Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×64 256×256×64 256×256×64 Conv 3×3×1 256×256×1 256×256×1 表 3 各算法平均PSNR和SSIM
Table 3 PSNR and SSIM of each algorithm
Algorithm BFTH MHE DLS DMRN Ours PSNR/dB 32.93 33.92 34.39 34.86 35.90 SSIM 0.828 0.858 0.887 0.903 0.928 表 4 各算法平均粗糙度
Table 4 Roughness of each algorithm
Algorithm BFTH MHE DLS DMRN Ours ρ 0.107 0.072 0.064 0.058 0.049 -
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