The Third Challenge on Image Denoising at NTIRE 2026: Methods and Results
For the image denoising community, this challenge provides a comprehensive benchmark of the latest unconstrained methods, but it is an incremental update to existing challenge series.
The NTIRE 2026 Challenge on Image Denoising benchmarked advanced neural architectures for high-noise regime (σ=50), with 20 finalist teams from 116 registrants, achieving state-of-the-art PSNR performance without constraints on model size or compute.
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.