Abstract
Deep (convolutional) neural networks (DCNN) have recently gained popularity, and shown improved performance in the field of image enhancement (de-noising and super-resolution, for instance). However, the central issue of recovering finer texture details in images still remains unsolved. State-of-the-art objective functions used in DCNN mostly focus on minimizing the mean squared reconstruction error. The resulting image estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details, and are therefore error-prone with respect to fine-scale, possibly clinically relevant details. In this article, we present GANReDL, a generative adversarial network (GAN) for image enhancement equipped with a real-order derivative induced loss functions (ReDL) which we will show gives improved images, in particular wrt to the reconstruction of fine-scale details. To the best of our knowledge, this is the first framework that incorporates non-integer order derivatives in loss functions. To this aim, we propose a discriminator network that is trained to differentiate between the enhanced images and ground-truth images, and propose a new loss function motivated by real-order derivatives which is capable of also capturing global image features rather than pixel-wise features only. We show, with several numerical experiments, that GANReDL is better in reconstructing the high-frequency image details, and therefore show improved performance for image enhancement over other state-of-the-art methods.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – |
Subtitle of host publication | MICCAI 2019 - 22nd International Conference, Proceedings |
Editors | Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 110-117 |
Number of pages | 8 |
ISBN (Electronic) | 9783030322489 |
ISBN (Print) | 9783030322472 |
DOIs | |
Publication status | Published - 10 Oct 2019 |
Event | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019 https://www.miccai2019.org/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11766 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 |
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Country/Territory | China |
City | Shenzhen |
Period | 13/10/19 → 17/10/19 |
Internet address |
Keywords
- Generative adversarial networks
- Medical image super-resolution
- Real order derivative operators
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science