Non-Uniformity Correction of Infrared Images Based on Improved CNN With Long-Short Connections

Non-uniformity is a common phenomenon in infrared imaging system, which seriously affects imaging quality. In view of the problems of existing non-uniformity correction of infrared images, such as loss of image details and blurred edge of image, an improved non-uniformity correction method of infrared images based on convolution neural network using long-short connections (LSC-CNN) is proposed. The proposed method designs a long-short connection residual network structure suitable for non-uniformity correction of infrared image.The network depth is increased to fully learn the noise by short connections, image sizes are adjusted to reduce the number of parameters, the long connection is used to solve the problem of image information loss caused by transposed convolution, and a multiply operation is carried out to enhance the contrast of corrected images. Besides, batch normalization is utilized to improve the training speed. The experimental results show that LSC-CNN has excellent performance in non-uniformity correction of infrared images whether qualitative evaluation or quantitative evaluation. LSC-CNN is especially effective in image detail preservation and image edge protection whose average PSNR exceeds 37.5 dB and the average SSIM is greater than 0.98.

Automated summary

LSC-CNN is an effective method for non-uniformity correction of infrared images, with advantages in detail preservation and edge protection, showing excellent performance in both qualitative and quantitative evaluation.