超越模型设计：数据导向训练与自我集成用于高斯色图像去噪
Beyond Model Design: Data-Centric Training and Self-Ensemble for Gaussian Color Image Denoising
AI总结：
本文提出解决NTIRE 2026图像去噪挑战的新方法，通过更强大的数据导向训练和更完整的测试时间能力释放，提升Restormer架构性能。在固定噪声水平σ=50下，通过扩大训练数据集和两阶段优化，使PSNR提升至30.762 dB，SSIM达0.861。
摘要：
本文提出解决NTIRE 2026图像去噪挑战的新方法，通过更强大的数据导向训练和更完整的测试时间能力释放，提升Restormer架构性能。在固定噪声水平σ=50下，通过扩大训练数据集和两阶段优化，使PSNR提升至30.762 dB，SSIM达0.861。消融研究显示，主要增益来自扩大训练数据集和两阶段优化计划，自我集成提供边际但一致的改进。
英文摘要：
This paper presents our solution to the NTIRE 2026 Image Denoising Challenge (Gaussian color image denoising at fixed noise level $σ= 50$). Rather than proposing a new restoration backbone, we revisit the performance boundary of the mature Restormer architecture from two complementary directions: stronger data-centric training and more complete Test-Time capability release. Starting from the public Restormer $σ\!=\!50$ baseline, we expand the standard multi-dataset training recipe with larger and more diverse public image corpora and organize optimization into two stages. At inference, we apply $\times 8$ geometric self-ensemble to further release model capacity. A TLC-style local inference wrapper is retained for implementation consistency; however, systematic ablation reveals its quantitative contribution to be negligible in this setting. On the challenge validation set of 100 images, our final submission achieves 30.762 dB PSNR and 0.861 SSIM, improving over the public Restormer $σ\!=\!50$ pretrained baseline by up to 3.366 dB PSNR. Ablation studies show that the dominant gain originates from the expanded training corpus and the two-stage optimization schedule, and self-ensemble provides marginal but consistent improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
