Video restoration based on deep learning: a comprehensive survey

Rota, Claudio; Buzzelli, Marco; Bianco, Simone; Schettini, Raimondo

doi:10.21203/rs.3.rs-1714666/v1

Cited by 2 publications

(1 citation statement)

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“…Inspired by [20], STOPN predicts spatio-temporal offsets that are different at each spatial and temporal position. Moreover, as stated by [59], they apply deformable alignment at feature level to increase alignment accuracy. The third module contains two groups of standard and transposed convolutions to progressively perform feature fusion and upscaling.…”

Section: Casia Lcvg Team [58]mentioning

confidence: 99%

NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Yang¹,

Timofte²,

Zheng³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compressed image, and Track 2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets.In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 videos) and 30 additional videos. In this challenge, there are 12 teams and 2 teams that submitted the final results to Track 1 and Track 2, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution on compressed image and video. The proposed LDV 3.0 dataset is available at https: //github.com/RenYang-home/LDV_dataset. The homepage of this challenge is at https://github.com/RenYang-home/AIM22_CompressSR.

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Section: Casia Lcvg Team [58]mentioning

confidence: 99%