Title
Deep Stereoscopic Image Super-Resolution via Interaction Module
Abstract
Deep learning-based methods have achieved remarkable performance in single image super-resolution. However, these methods cannot be effectively applied in stereoscopic image super-resolution without considering the characteristics of stereoscopic images. In this article, an interaction module-based stereoscopic image super-resolution network (IMSSRnet) is proposed to effectively utilize the correlation information in stereoscopic images. The key insight of the network lies with how to explore the complementary information of one view to help the reconstruction of another view. Thus, an interaction module is designed to acquire the enhanced features by utilizing complementary information between different views. Specifically, the interaction module is composed of a series of interaction units with a residual structure. In addition, the single image features of left and right views are obtained by a spatial feature extraction module, which can be realized by any existing single image super-resolution models. In order to obtain high-quality stereoscopic images, a gradient loss is introduced to preserve the texture details in a view, and a disparity loss is developed to constrain the disparity relationship between different views. Experimental results demonstrate that the proposed method achieves a promising performance and outperforms the state-of-the-art methods.
Year
DOI
Venue
2021
10.1109/TCSVT.2020.3037068
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
DocType
Volume
Super-resolution,stereoscopic image,interaction module,deep learning
Journal
31
Issue
ISSN
Citations 
8
1051-8215
4
PageRank 
References 
Authors
0.38
0
7
Name
Order
Citations
PageRank
Jianjun Lei171352.69
Zhe Zhang23111.51
Xiaoting Fan3123.24
Bolan Yang440.38
Xinxin Li5278.16
Ying Chen63613.36
Qingming Huang73919267.71