Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution. / Chen, Zhen; Guo, Xiaoqing; Yang, Chen; Ibragimov, Bulat; Yuan, Yixuan.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. red. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer VS, 2020. s. 184-193 (Lecture Notes in Computer Science, Bind 12265 LNCS).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Chen, Z, Guo, X, Yang, C, Ibragimov, B & Yuan, Y 2020, Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution. i AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (red), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer VS, Lecture Notes in Computer Science, bind 12265 LNCS, s. 184-193, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 04/10/2020. https://doi.org/10.1007/978-3-030-59722-1_18

APA

Chen, Z., Guo, X., Yang, C., Ibragimov, B., & Yuan, Y. (2020). Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution. I A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (red.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings (s. 184-193). Springer VS. Lecture Notes in Computer Science Bind 12265 LNCS https://doi.org/10.1007/978-3-030-59722-1_18

Vancouver

Chen Z, Guo X, Yang C, Ibragimov B, Yuan Y. Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution. I Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, red., Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer VS. 2020. s. 184-193. (Lecture Notes in Computer Science, Bind 12265 LNCS). https://doi.org/10.1007/978-3-030-59722-1_18

Author

Chen, Zhen ; Guo, Xiaoqing ; Yang, Chen ; Ibragimov, Bulat ; Yuan, Yixuan. / Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. red. / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer VS, 2020. s. 184-193 (Lecture Notes in Computer Science, Bind 12265 LNCS).

Bibtex

@inproceedings{2fb39d0f20504619a0d5093553745ade,
title = "Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution",
abstract = "Super-Resolution (SR) techniques can compensate for the missing information of low-resolution images and further promote experts and algorithms to make accurate diagnosis decisions. Although the existing pixel-loss based SR works produce high-resolution images with impressive objective metrics, the over-smoothed contents that lose high-frequency information would disturb the visual experience and the subsequent diagnosis. To address this issue, we propose a joint Spatial-Wavelet super-resolution Network (SWD-Net) with collaborative Dual-stream. In the spatial stage, a Refined Context Fusion (RCF) is proposed to iteratively rectify the features by a counterpart stream with compensative receptive fields. After that, the wavelet stage enhances the reconstructed images, especially the structural boundaries. Specifically, we design the tailor-made Wavelet Features Adaptation (WFA) to adjust the wavelet coefficients for better compatibility with networks and Wavelet-Aware Convolutional blocks (WAC) to exploit features in the wavelet domain efficiently. We further introduce the wavelet coefficients supervision together with the traditional spatial loss to jointly optimize the network and obtain the high-frequency enhanced SR images. To evaluate the SR for medical images, we build a benchmark dataset with histopathology images and evaluate the proposed SWD-Net under different settings. The comprehensive experiments demonstrate our SWD-Net outperforms state-of-the-art methods. Furthermore, SWD-Net is proven to promote medical image diagnosis with a large margin. The source code and dataset are available at https://github.com/franciszchen/SWD-Net.",
keywords = "Convolutional neural networks, Super-resolution, Wavelet domain",
author = "Zhen Chen and Xiaoqing Guo and Chen Yang and Bulat Ibragimov and Yixuan Yuan",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",
year = "2020",
doi = "10.1007/978-3-030-59722-1_18",
language = "English",
isbn = "9783030597214",
series = "Lecture Notes in Computer Science",
publisher = "Springer VS",
pages = "184--193",
editor = "Martel, {Anne L.} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, {Maria A.} and Zhou, {S. Kevin} and Daniel Racoceanu and Leo Joskowicz",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",

}

RIS

TY - GEN

T1 - Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution

AU - Chen, Zhen

AU - Guo, Xiaoqing

AU - Yang, Chen

AU - Ibragimov, Bulat

AU - Yuan, Yixuan

N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - Super-Resolution (SR) techniques can compensate for the missing information of low-resolution images and further promote experts and algorithms to make accurate diagnosis decisions. Although the existing pixel-loss based SR works produce high-resolution images with impressive objective metrics, the over-smoothed contents that lose high-frequency information would disturb the visual experience and the subsequent diagnosis. To address this issue, we propose a joint Spatial-Wavelet super-resolution Network (SWD-Net) with collaborative Dual-stream. In the spatial stage, a Refined Context Fusion (RCF) is proposed to iteratively rectify the features by a counterpart stream with compensative receptive fields. After that, the wavelet stage enhances the reconstructed images, especially the structural boundaries. Specifically, we design the tailor-made Wavelet Features Adaptation (WFA) to adjust the wavelet coefficients for better compatibility with networks and Wavelet-Aware Convolutional blocks (WAC) to exploit features in the wavelet domain efficiently. We further introduce the wavelet coefficients supervision together with the traditional spatial loss to jointly optimize the network and obtain the high-frequency enhanced SR images. To evaluate the SR for medical images, we build a benchmark dataset with histopathology images and evaluate the proposed SWD-Net under different settings. The comprehensive experiments demonstrate our SWD-Net outperforms state-of-the-art methods. Furthermore, SWD-Net is proven to promote medical image diagnosis with a large margin. The source code and dataset are available at https://github.com/franciszchen/SWD-Net.

AB - Super-Resolution (SR) techniques can compensate for the missing information of low-resolution images and further promote experts and algorithms to make accurate diagnosis decisions. Although the existing pixel-loss based SR works produce high-resolution images with impressive objective metrics, the over-smoothed contents that lose high-frequency information would disturb the visual experience and the subsequent diagnosis. To address this issue, we propose a joint Spatial-Wavelet super-resolution Network (SWD-Net) with collaborative Dual-stream. In the spatial stage, a Refined Context Fusion (RCF) is proposed to iteratively rectify the features by a counterpart stream with compensative receptive fields. After that, the wavelet stage enhances the reconstructed images, especially the structural boundaries. Specifically, we design the tailor-made Wavelet Features Adaptation (WFA) to adjust the wavelet coefficients for better compatibility with networks and Wavelet-Aware Convolutional blocks (WAC) to exploit features in the wavelet domain efficiently. We further introduce the wavelet coefficients supervision together with the traditional spatial loss to jointly optimize the network and obtain the high-frequency enhanced SR images. To evaluate the SR for medical images, we build a benchmark dataset with histopathology images and evaluate the proposed SWD-Net under different settings. The comprehensive experiments demonstrate our SWD-Net outperforms state-of-the-art methods. Furthermore, SWD-Net is proven to promote medical image diagnosis with a large margin. The source code and dataset are available at https://github.com/franciszchen/SWD-Net.

KW - Convolutional neural networks

KW - Super-resolution

KW - Wavelet domain

UR - http://www.scopus.com/inward/record.url?scp=85092733435&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-59722-1_18

DO - 10.1007/978-3-030-59722-1_18

M3 - Article in proceedings

AN - SCOPUS:85092733435

SN - 9783030597214

T3 - Lecture Notes in Computer Science

SP - 184

EP - 193

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings

A2 - Martel, Anne L.

A2 - Abolmaesumi, Purang

A2 - Stoyanov, Danail

A2 - Mateus, Diana

A2 - Zuluaga, Maria A.

A2 - Zhou, S. Kevin

A2 - Racoceanu, Daniel

A2 - Joskowicz, Leo

PB - Springer VS

T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

Y2 - 4 October 2020 through 8 October 2020

ER -

ID: 271604171