@inproceedings{95cbc122bbbe4236a5ff1611a66a1150,
title = "CNN Based Multi-view Image Quality Enhancement",
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.",
keywords = "Convolutional Neural Networks(CNN), Image quality enhancement, Keywords, multi-view images",
author = "Jeon, {Gyu Lee} and Kim, {Hee Jae} and Eun Yeo and Kang, {Je Won}",
note = "Funding Information: ACKNOWLEDGMENT This work was supported by Institute of Information communications Technology Planning Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2020-0-00920, Development of Ultra High Resolution Unstructured Plenoptic 9ideo Storage Compression Streaming Technology for Medium to Large Space). This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2022R1A2C4002052). Publisher Copyright: {\textcopyright} 2022 IEEE.; null ; Conference date: 05-07-2022 Through 08-07-2022",
year = "2022",
doi = "10.1109/ICUFN55119.2022.9829639",
language = "English",
series = "International Conference on Ubiquitous and Future Networks, ICUFN",
publisher = "IEEE Computer Society",
pages = "372--375",
booktitle = "ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks",
}