Non-Uniformity Correction of Spatial Object Images Using Multi-Scale Residual Cycle Network (CycleMRSNet)

Ground-based telescopes often encounter challenges such as stray light and vignetting when capturing space objects, leading to non-uniform image backgrounds. This not only weakens the signal-to-noise ratio for target tracking but also reduces the accuracy of recognition systems. To address this challenge, We have proposed a novel network architecture called CycleMRSNet, which is based on the CycleGAN framework and incorporates a multi-scale attention mechanism to enhance image processing capabilities. Specifically, we have introduced a multi-scale feature extraction module (MSFEM) at the front end of the generator and embedded an efficient multi-scale attention residual block (EMA-residual block) within the Resnet backbone network. This design improves the efficiency of feature extraction and increases the focus on multi-scale information in high-dimensional feature maps, enabling the network to more comprehensively understand and concentrate on key areas within images, thereby capably correcting non-uniform backgrounds. To evaluate the performance of CycleMRSNet, we trained the model using a small-scale dataset and conducted corrections on simulated and real images within the test set. Experimental results showed that our model achieved scores of PSNR 32.7923, SSIM 0.9814, and FID 1.9212 in the test set, outperforming other methods. These metrics suggest that our approach significantly improves the correction of non-uniform backgrounds and enhances the robustness of the system.