Cross-View Hierarchy Network for Stereo Image Super-Resolution
- URL: http://arxiv.org/abs/2304.06236v1
- Date: Thu, 13 Apr 2023 03:11:30 GMT
- Title: Cross-View Hierarchy Network for Stereo Image Super-Resolution
- Authors: Wenbin Zou, Hongxia Gao, Liang Chen, Yunchen Zhang, Mingchao Jiang,
Zhongxin Yu, and Ming Tan
- Abstract summary: Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views.
We propose a novel method, named Cross-View-Hierarchy Network for Stereo Image Super-Resolution (CVHSSR)
CVHSSR achieves the best stereo image super-resolution performance than other state-of-the-art methods while using fewer parameters.
- Score: 14.574538513341277
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Stereo image super-resolution aims to improve the quality of high-resolution
stereo image pairs by exploiting complementary information across views. To
attain superior performance, many methods have prioritized designing complex
modules to fuse similar information across views, yet overlooking the
importance of intra-view information for high-resolution reconstruction. It
also leads to problems of wrong texture in recovered images. To address this
issue, we explore the interdependencies between various hierarchies from
intra-view and propose a novel method, named Cross-View-Hierarchy Network for
Stereo Image Super-Resolution (CVHSSR). Specifically, we design a
cross-hierarchy information mining block (CHIMB) that leverages channel
attention and large kernel convolution attention to extract both global and
local features from the intra-view, enabling the efficient restoration of
accurate texture details. Additionally, a cross-view interaction module (CVIM)
is proposed to fuse similar features from different views by utilizing
cross-view attention mechanisms, effectively adapting to the binocular scene.
Extensive experiments demonstrate the effectiveness of our method. CVHSSR
achieves the best stereo image super-resolution performance than other
state-of-the-art methods while using fewer parameters. The source code and
pre-trained models are available at https://github.com/AlexZou14/CVHSSR.
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