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:
this https URL

本研究提出了一种利用事件信号进行纹理增强的视频超分辨率方法，通过逐步探索高时间分辨率的事件信息以实现纹理修复，从而获得更精确和丰富的高分辨率细节，实验证明该方法在四个数据集上达到了最先进的性能。