Video Super Resolution Based on Deep Learning: A Comprehensive Survey
- URL: http://arxiv.org/abs/2007.12928v3
- Date: Wed, 16 Mar 2022 15:07:21 GMT
- Title: Video Super Resolution Based on Deep Learning: A Comprehensive Survey
- Authors: Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan
Liu, Linlin Yang, Radu Timofte
- Abstract summary: We comprehensively investigate 33 state-of-the-art video super-resolution (VSR) methods based on deep learning.
We propose a taxonomy and classify the methods into six sub-categories according to the ways of utilizing inter-frame information.
We summarize and compare the performance of the representative VSR method on some benchmark datasets.
- Score: 87.30395002197344
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, deep learning has made great progress in many fields such as
image recognition, natural language processing, speech recognition and video
super-resolution. In this survey, we comprehensively investigate 33
state-of-the-art video super-resolution (VSR) methods based on deep learning.
It is well known that the leverage of information within video frames is
important for video super-resolution. Thus we propose a taxonomy and classify
the methods into six sub-categories according to the ways of utilizing
inter-frame information. Moreover, the architectures and implementation details
of all the methods are depicted in detail. Finally, we summarize and compare
the performance of the representative VSR method on some benchmark datasets. We
also discuss some challenges, which need to be further addressed by researchers
in the community of VSR. To the best of our knowledge, this work is the first
systematic review on VSR tasks, and it is expected to make a contribution to
the development of recent studies in this area and potentially deepen our
understanding to the VSR techniques based on deep learning.
Related papers
- Towards Generalist Robot Learning from Internet Video: A Survey [56.621902345314645]
This survey presents an overview of methods for learning from video (LfV) in the context of reinforcement learning (RL) and robotics.
We focus on methods capable of scaling to large internet video datasets.
We advocate for scalable foundation model approaches that can leverage the full range of internet video data.
arXiv Detail & Related papers (2024-04-30T15:57:41Z) - Deep Learning for Video-Text Retrieval: a Review [13.341694455581363]
Video-Text Retrieval (VTR) aims to search for the most relevant video related to the semantics in a given sentence.
In this survey, we review and summarize over 100 research papers related to VTR.
arXiv Detail & Related papers (2023-02-24T10:14:35Z) - Guided Depth Map Super-resolution: A Survey [88.54731860957804]
Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image.
A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques.
This survey is an effort to present a comprehensive survey of recent progress in GDSR.
arXiv Detail & Related papers (2023-02-19T15:43:54Z) - Benchmark Dataset and Effective Inter-Frame Alignment for Real-World
Video Super-Resolution [65.20905703823965]
Video super-resolution (VSR) aiming to reconstruct a high-resolution (HR) video from its low-resolution (LR) counterpart has made tremendous progress in recent years.
It remains challenging to deploy existing VSR methods to real-world data with complex degradations.
EAVSR takes the proposed multi-layer adaptive spatial transform network (MultiAdaSTN) to refine the offsets provided by the pre-trained optical flow estimation network.
arXiv Detail & Related papers (2022-12-10T17:41:46Z) - Advances and Challenges in Deep Lip Reading [2.930266486910376]
This paper provides a comprehensive survey of the state-of-the-art deep learning based Visual Speech Recognition research.
We focus on data challenges, task-specific complications, and the corresponding solutions.
We also discuss the main modules of a VSR pipeline and the influential datasets.
arXiv Detail & Related papers (2021-10-15T06:18:26Z) - A Survey on Deep Learning Technique for Video Segmentation [147.0767454918527]
Video segmentation plays a critical role in a broad range of practical applications.
Deep learning based approaches have been dedicated to video segmentation and delivered compelling performance.
arXiv Detail & Related papers (2021-07-02T15:51:07Z) - Video Summarization Using Deep Neural Networks: A Survey [72.98424352264904]
Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content.
This work focuses on the recent advances in the area and provides a comprehensive survey of the existing deep-learning-based methods for generic video summarization.
arXiv Detail & Related papers (2021-01-15T11:41:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.