# NTIRE 2021 Challenge on Image Deblurring Source: [NTIRE 2021 Challenge on Image Deblurring]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:09:38.543Z Authors: Seungjun Nah, Sanghyun Son, Suyoung Lee, Radu Timofte, Kyoung Mu Lee Published: 2021-04-30T09:12:53.000Z ## Abstract Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Deblurring on NTIRE Image Deblurring Challenge Track 2 (JPEG Artifacts) 2021 (test)](): Image Deblurring; NTIRE Image Deblurring Challenge Track 2 (JPEG Artifacts) 2021 (test); PSNR: 29.62. - [Image Deblurring on NTIRE Image Deblurring Challenge Track 2 REDS 300 2021 (dev)](): Image Deblurring; NTIRE Image Deblurring Challenge Track 2 REDS 300 2021 (dev); PSNR: 29.17. ## Original sources and implementation [Original paper]() [PDF]()