级联残差学习的红外图像非均匀性校正方法

doi:10.19665/j.issn1001-2400.2019.01.003

Abstract

Abstract:

Traditional scene adaptive nonuniformity correction methods generally suffer from the over smooth and residual nonuniformity in the corrected results. In view of this, a cascade residual learning based nonuniformity correction method is presented. This method uses the multiscale feature extraction unit to fuse the extracted features and employs the residual learning strategy to deal with the overfitting problem. Experimental results validate that the proposed method yields nearly 5dB improvement in the average peak signal-to-noise ratio (PSNR) as compared to the traditional scene adaptive correction methods. Moreover, its visual effects are clearer and sharper.

Key words: deep learning, nonuniformity correction, image denoising, infrared image processing

CLC Number:

TN215

LAI Rui,GUAN Juntao,XU Kunran,XIONG Ai,YANG Yintang. Cascade residual learning method for infrared image nonuniformity correction[J].Journal of Xidian University, 2019, 46(1): 14-19.

Figures/Tables 6

References 13

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评价指标	污染图像	校正后图像
评价指标	污染图像	NN-NUC	TV-NUC	文中方法
峰值信噪比/dB	20.29	28.10	28.31	33.25
粗糙度	0.2760	0.1093	0.1083	0.0835

图像序列	图像分辨率	处理速度/(帧·秒^-1)
图像序列	图像分辨率	NN-NUC	TV-NUC	文中方法
测试序列1	320×256	628	549	42
测试序列2	384×288	580	443	29

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Cascade residual learning method for infrared image nonuniformity correction

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