Abstract
We propose a CNN-based multi-view image quality enhancement (MVIQE) to improve the quality of a target image using adjacent multi-view images. Differing from the conventional single frame quality enhancement, our approach aims to improve the quality by transferring a higher quality of other input views in multi-view images as references. Our network contains an optical flow estimation module, warping layers, and image synthesis module to enhance the quality of a target image with quantization noise. Experimental results show that our method outperforms previous studies on image quality enhancement in terms of peak signal-to-noise ratio performance.
| Original language | English |
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| Title of host publication | ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks |
| Publisher | IEEE Computer Society |
| Pages | 372-375 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781665485500 |
| DOIs | |
| State | Published - 2022 |
| Event | 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022 - Virtual, Barcelona, Spain Duration: 5 Jul 2022 → 8 Jul 2022 |
Publication series
| Name | International Conference on Ubiquitous and Future Networks, ICUFN |
|---|---|
| Volume | 2022-July |
| ISSN (Print) | 2165-8528 |
| ISSN (Electronic) | 2165-8536 |
Conference
| Conference | 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022 |
|---|---|
| Country/Territory | Spain |
| City | Virtual, Barcelona |
| Period | 5/07/22 → 8/07/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Convolutional Neural Networks(CNN)
- Image quality enhancement
- Keywords
- multi-view images