Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
Not on the map. Why?
Abstract (arXiv)
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale-agnostic, i.e. one can train over a continuous range of scales and subsequently query at arbitrary resolutions. Due to the difficulty of performing super-resolution on inherently noisy data, we analyze network behavior under multiple denoising strategies. Lastly we compare this method to a standard convolutional decoder using both quantitative metrics and a radiologist study implemented in Voxel, our newly developed tool for web-based evaluation of medical images.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 17 Oct 2022.
Builds on
18 of 25 references.
References · 18
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
- 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
- 2020Pre-Trained Image Processing Transformercited by 891
- 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 550
- 2020Learned Initializations for Optimizing Coordinate-Based Neural Representationscited by 240
- 2021NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstructioncited by 145
- 2021BACON: Band-limited Coordinate Networks for Multiscale Scene Representationcited by 142
- 2021Modulated Periodic Activations for Generalizable Local Functional Representationscited by 140
- 2022Any-resolution Training for High-resolution Image Synthesiscited by 72
- 2021Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fieldscited by 69
6 more
- 2018Compressed Sensing with Deep Image Prior and Learned Regularizationcited by 47
- 2021IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representationcited by 42
- 2022SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluationcited by 42
- 2019Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generatorscited by 40
- 2022Zero-shot Blind Image Denoising via Implicit Neural Representationscited by 12
- 2021Learning Continuous Representation of Audio for Arbitrary Scale Super Resolutioncited by 9
Built on by
Citing papers per year: 2023: 2.
2 papers.