This paper reports on the NTIRE 2026 Challenge on Image Denoising, specifically focusing on the high-noise regime ($σ= 50$). The competition investigates advanced neural architectures designed to restore high-fidelity details from images corrupted by additive white Gaussian noise (AWGN). Unlike constrained benchmarks, this track emphasizes peak quantitative performance, measured by Peak Signal-to-Noise Ratio (PSNR), without limitations on parameter count or computational overhead. By synthesizing contributions from 20 finalist teams out of 116 registrants, this report benchmarks the latest technical innovations and provides a comprehensive snapshot of the current state-of-the-art in unconstrained image restoration.

翻译：本文报告了NTIRE 2026图像去噪挑战赛的相关工作，重点关注高噪声场景（σ=50）。该比赛探究了专为从加性高斯白噪声（AWGN）破坏图像中恢复高保真细节而设计的先进神经架构。与受限基准不同，本赛道强调峰值定量性能（以峰值信噪比（PSNR）衡量），且不对参数量或计算开销设限。通过综合116名注册参赛者中20支决赛队伍的研究成果，本文对最新技术革新进行了基准测试，并全面呈现了当前非受限图像复原领域的最先进水平。