NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Citations Over Time

Abstract

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.

Related Papers

• → A benchmark for comparison of cell tracking algorithms(2014)428 cited
• → Content-Noise Complementary Learning for Medical Image Denoising(2021)167 cited
• → Multi-Focus Image Fusion: Algorithms, Evaluation, and a Library(2020)25 cited
• → Cleaning Landmark and Geolocation Dataset using Deep Learning Methods(2021)