Name
Abstract | ||
|---|---|---|
ABSTRACTUnder stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorporating this information for stereo image JPEG artifacts removal is a huge challenge, since the existing compression artifacts make pixel-level view alignment difficult. In this paper, we propose a novel parallax transformer network (PTNet) to integrate the information from stereo image pairs for stereo image JPEG artifacts removal. Specifically, a well-designed symmetric bi-directional parallax transformer module is proposed to match features with similar textures between different views instead of pixel-level view alignment. Due to the issues of occlusions and boundaries, a confidence-based cross-view fusion module is proposed to achieve better feature fusion for both views, where the cross-view features are weighted with confidence maps. Especially, we adopt a coarse-to-fine design for the cross-view interaction, leading to better performance. Comprehensive experimental results demonstrate that our PTNet can effectively remove compression artifacts and achieves superior performance than other testing state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3503161.3547986 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuhao Jiang | 1 | 0 | 0.34 |
| Weimin Tan | 2 | 36 | 11.76 |
| Ri Cheng | 3 | 0 | 1.35 |
| Shili Zhou | 4 | 0 | 0.68 |
| Bo Yan | 5 | 44 | 9.36 |