EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
Event-based vision has drawn increasing attention due to its unique
characteristics, such as high temporal resolution and high dynamic range. It
has been used in video super-resolution (VSR) recently to enhance the flow
estimation and temporal alignment. Rather than for motion learning, we propose
in this paper the first VSR method that utilizes event signals for texture
enhancement. Our method, called EvTexture, leverages high-frequency details of
events to better recover texture regions in VSR. In our EvTexture, a new
texture enhancement branch is presented. We further introduce an iterative
texture enhancement module to progressively explore the
high-temporal-resolution event information for texture restoration. This allows
for gradual refinement of texture regions across multiple iterations, leading
to more accurate and rich high-resolution details. Experimental results show
that our EvTexture achieves state-of-the-art performance on four datasets. For
the Vid4 dataset with rich textures, our method can get up to 4.67dB gain
compared with recent event-based methods. Code:
https://github.com/DachunKai/EvTexture.
667914da6a778904d7d6acfa
Dachun Kai
claimed_verified
2024-06-24T07:27:15.111Z
64f596f3bab7e6b905d9794b
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/MSTZ1H1JVaANlz2OGt3A3.jpeg
Dachun Kai
user
BoyDachun
667914da6a778904d7d6acfb
Jiayao Lu
667914da6a778904d7d6acfc
Yueyi Zhang
admin_assigned
2024-06-24T13:38:38.304Z
661cf278c153e4a0a2c31abf
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Yueyi Zhang
user
zhyuey
667914da6a778904d7d6acfd
Xiaoyan Sun
EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
Event-based vision has drawn increasing attention due to its unique
characteristics, such as high temporal resolution and high dynamic range. It
has been used in video super-resolution (VSR) recently to enhance the flow
estimation and temporal alignment. Rather than for motion learning, we propose
in this paper the first VSR method that utilizes event signals for texture
enhancement. Our method, called EvTexture, leverages high-frequency details of
events to better recover texture regions in VSR. In our EvTexture, a new
texture enhancement branch is presented. We further introduce an iterative
texture enhancement module to progressively explore the
high-temporal-resolution event information for texture restoration. This allows
for gradual refinement of texture regions across multiple iterations, leading
to more accurate and rich high-resolution details. Experimental results show
that our EvTexture achieves state-of-the-art performance on four datasets. For
the Vid4 dataset with rich textures, our method can get up to 4.67dB gain
compared with recent event-based methods. Code:
https://github.com/DachunKai/EvTexture.
A new video super-resolution method uses event signals to enhance texture recovery, achieving state-of-the-art results on several datasets.
event-based vision video super-resolution texture enhancement high-frequency details texture enhancement branch iterative texture enhancement module