Abstract
In the infrared focal plane arrays imaging systems, the temperature-dependent nonuniformity effects severely degrade the image quality. In this letter, we propose a very deep convolutional neural network for unified infrared aerothermal nonuniform correction. Our network is built with the multiscale and residual training. The multiscale subnetworks utilize the multiscale property in the images, and the long–short-term residual learning contributes to the information propagation. Compared with the previous methods, the proposed method is more robust to various nonuniform artifacts and more efficient at processing time. Experimental results validate the superiority of our method for infrared nonuniform correction.
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