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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

3 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020
Subtitle of host publication23rd International Conference Lima, Peru, October 4–8, 2020 Proceedings, Part V
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer
Pages184-193
ISBN (Electronic)9783030597221
ISBN (Print)9783030597214
Publication statusPublished - Oct 2020

Publication series

NameLecture Notes in Computer Science
Volume12265
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title23rd International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI 2020)
PlacePeru
CityLima
Period4 - 8 October 2020

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.

Research Area(s)

  • Convolutional neural networks, Super-resolution, Wavelet domain

Citation Format(s)

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 Lima, Peru, October 4–8, 2020 Proceedings, Part V. ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer, 2020. p. 184-193 (Lecture Notes in Computer Science; Vol. 12265).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review