We study CT image denoising in the unpaired and self-supervised regimes by evaluating two strong, training-data-efficient paradigms: a CycleGAN-based residual translator and a Noise2Score (N2S) score-matching denoiser. Under a common evaluation protocol, a configuration sweep identifies a simple standard U-Net backbone within CycleGAN (lambda_cycle = 30, lambda_iden = 2, ngf = ndf = 64) as the most reliable setting; we then train it to convergence with a longer schedule. The selected CycleGAN improves the noisy input from 34.66 dB / 0.9234 SSIM to 38.913 dB / 0.971 SSIM and attains an estimated score of 1.9441 and an unseen-set (Kaggle leaderboard) score of 1.9343. Noise2Score, while slightly behind in absolute PSNR / SSIM, achieves large gains over very noisy inputs, highlighting its utility when clean pairs are unavailable. Overall, CycleGAN offers the strongest final image quality, whereas Noise2Score provides a robust pair-free alternative with competitive performance. Source code is available at https://github.com/hanifsyarubany/CT-Scan-Image-Denoising-using-CycleGAN-and-Noise2Score.

翻译：本研究通过评估两种强效且训练数据高效的范式——基于CycleGAN的残差转换器与Noise2Score（N2S）分数匹配去噪器，探讨了非配对与自监督机制下的CT图像去噪问题。在统一评估协议下，通过参数扫描确定了CycleGAN中采用标准U-Net骨干网络（λ_cycle=30，λ_iden=2，ngf=ndf=64）为最稳定配置；随后通过延长训练周期使其充分收敛。优化后的CycleGAN将噪声输入图像的指标从34.66 dB/0.9234 SSIM提升至38.913 dB/0.971 SSIM，并取得1.9441的预估分数及1.9343的未见数据集（Kaggle排行榜）分数。Noise2Score虽然在绝对PSNR/SSIM指标上略逊，但在处理高噪声输入时展现出显著增益，凸显了其在缺乏干净配对数据时的实用价值。总体而言，CycleGAN能提供最优的最终图像质量，而Noise2Score作为无需配对数据的鲁棒性替代方案，其性能表现同样具有竞争力。源代码已发布于https://github.com/hanifsyarubany/CT-Scan-Image-Denoising-using-CycleGAN-and-Noise2Score。