The existing nonuniformity correction methods generally have the defects of image blur, artifacts, image over-smoothing, and nonuniform residuals. It is difficult for these methods to meet the requirements of image enhancement in various complex application scenarios. In particular, when these methods are applied to dim small target images, they may remove dim small targets as noise points due to the image over-smoothing. This paper draws on the idea of a residual network and proposes a

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... e learning network based on the imaging mechanism of an infrared line-scan system. We adopt a multi-scale feature extraction unit and design a gain correction sub-network and an offset correction sub-network, respectively. Then, we pre-train the two sub-networks independently. Finally, we cascade the two sub-networks into a two-stage network and train it. The experimental results show that the PSNR gain of our method can reach more than 15 dB, and it can achieve excellent performance in different backgrounds and different intensities of nonuniform noise. Moreover, our method can avoid losing texture details or dim small targets after effectively removing nonuniform noise.