mlsanityData to 14 Oct 2026

Perceptual cGAN for MRI Super-resolution

arXiv 2201.09314 ↗First posted 23 Jan 2022eess.IV cs.CV cs.LGcited by 3

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Abstract (arXiv)

Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients. Low-resolution MR imaging, by contrast, is faster than its high-resolution counterpart, but it compromises on fine details necessary for a more precise diagnosis. Super-resolution (SR), when applied to low-resolution MR images, can help increase their utility by synthetically generating high-resolution images with little additional time. In this paper, we present a SR technique for MR images that is based on generative adversarial networks (GANs), which have proven to be quite useful in generating sharp-looking details in SR. We introduce a conditional GAN with perceptual loss, which is conditioned upon the input low-resolution image, which improves the performance for isotropic and anisotropic MRI super-resolution.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 23 Jan 2022.

Builds on

14 of 23 references.

References · 14

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  3. 2014Conditional Generative Adversarial Netscited by 2,162
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  6. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  7. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  8. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
  9. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  10. 2018Residual Dense Network for Image Super-Resolutioncited by 799
  11. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 394
  12. 2018cGANs with Projection Discriminatorcited by 254
2 more
  1. 2018Deep Learning for Single Image Super-Resolution: A Brief Reviewcited by 158
  2. 2017Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resizecited by 37

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2022A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channelscited by 2
  2. 2023Transforming Breast Cancer Diagnosis: Towards Real-Time Ultrasound to Mammogram Conversion for Cost-Effective Diagnosiscited by 0
  3. 2024Untrained Perceptual Loss for image denoising of line-like structures in MR imagescited by 0