Abstract
Astronomical images are frequently affected by sensor noise, which can negatively impact the accuracy of subsequent data analysis. To address this issue, this study proposes an enhanced Pix2Pix generative adversarial network model that incorporates Residual Blocks and Self-Attention mechanisms to improve denoising performance. The effectiveness of the proposed model is evaluated by comparing it with traditional denoising methods, standard Pix2Pix, Pix2Pix with Residual Blocks, and Pix2Pix with Self-Attention using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. The findings demonstrate that the Pix2Pix model, when combined with both Residual Blocks and Self-Attention, significantly outperforms other models in noise reduction and detail preservation. This improved approach offers a robust solution for high-quality processing of astronomical images, providing clearer and more reliable data for scientific analysis. The results highlight the potential of advanced deep learning techniques in overcoming the challenges posed by sensor noise in astronomical imaging.
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