TempFormer: Temporally Consistent Transformer for Video Denoising
METADATA ONLY
Loading...
Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Video denoising is a low-level vision task that aims to restore high quality videos from noisy content. Vision Transformer (ViT) is a new machine learning architecture that has shown promising performance on both high-level and low-level image tasks. In this paper, we propose a modified ViT architecture for video processing tasks, introducing a new training strategy and loss function to enhance temporal consistency without compromising spatial quality. Specifically, we propose an efficient hybrid Transformer-based model, TempFormer, which composes Spatio-Temporal Transformer Blocks (STTB) and 3D convolutional layers. The proposed STTB learns the temporal information between neighboring frames implicitly by utilizing the proposed Joint Spatio-Temporal Mixer module for attention calculation and feature aggregation in each ViT block. Moreover, existing methods suffer from temporal inconsistency artifacts that are problematic in practical cases and distracting to the viewers. We propose a sliding block strategy with recurrent architecture, and use a new loss term, Overlap Loss, to alleviate the flickering between adjacent frames. Our method produces state-of-the-art spatio-temporal denoising quality with significantly improved temporal coherency, and requires less computational resources to achieve comparable denoising quality with competing methods (Fig. 1).
Permanent link
Publication status
published
External links
Book title
Computer Vision – ECCV 2022
Journal / series
Volume
13679
Pages / Article No.
481 - 496
Publisher
Springer
Event
17th European Conference on Computer Vision (ECCV 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Video denoising; Transformer; Temporal consistency