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20212026
most citedEfficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion

68 citations · 72 across the 6 of their papers we have counts for

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6 papers

cs.CV2026

NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge

Aleksei Khalin, Egor Ershov, Artyom Panshin +46

This paper presents a review of the NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge. The objective of the competition was to merge a set of misaligned smartphone images…

cs.CV2026

The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results

Xiang Chen, Hao Li, Jiangxin Dong +89

This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-wo…

cs.CV2026

NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results

Xin Li, Jiachao Gong, Xijun Wang +75

This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-…

cs.CV2024

NTIRE 2024 Challenge on Night Photography Rendering

Egor Ershov, Artyom Panshin, Oleg Karasev +47

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nig…

eess.IV2021★ 4 cited

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

Ren Yang, Radu Timofte, Jing Liu +69

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Di…

eess.IV2021★ 68 cited

Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion

Matteo Maggioni, Yibin Huang, Cheng Li +3

In recent years, denoising methods based on deep learning have achieved unparalleled performance at the cost of large computational complexity. In this work, we propose an Efficien…