# Impact of deep learning-based image super-resolution on binary signal detection

arXiv: https://arxiv.org/abs/2107.02338 · HTML: https://mlsanity.com/paper/2107.02338/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (2018)
- [Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks](https://mlsanity.com/paper/1710.01992/) (2017)
- [CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)](https://mlsanity.com/paper/1808.04256/) (2018)
- [On hallucinations in tomographic image reconstruction](https://mlsanity.com/paper/2012.00646/) (2020)
- [Real-World Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/2103.02368/) (2021)
- [Is Image Super-resolution Helpful for Other Vision Tasks?](https://mlsanity.com/paper/1509.07009/) (2015)
- [Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks](https://mlsanity.com/paper/2104.14037/) (2021)
- [Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods](https://mlsanity.com/paper/1905.06330/) (2019)
- [Task-Oriented Low-Dose CT Image Denoising](https://mlsanity.com/paper/2103.13557/) (2021)
- [Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks](https://mlsanity.com/paper/2001.09526/) (2020)

## Built on by (most cited first, 9 of 9)

- [Assessing the ability of generative adversarial networks to learn canonical medical image statistics](https://mlsanity.com/paper/2204.12007/) (2022)
- [Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT](https://mlsanity.com/paper/2303.02110/) (2023)
- [Prior image-based medical image reconstruction using a style-based generative adversarial network](https://mlsanity.com/paper/2202.08936/) (2022)
- [Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging](https://mlsanity.com/paper/2204.03547/) (2022)
- [Task-Based Assessment for Neural Networks: Evaluating Undersampled MRI Reconstructions based on Human Observer Signal Detection](https://mlsanity.com/paper/2210.12161/) (2022)
- [Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging](https://mlsanity.com/paper/2410.08329/) (2024)
- [A detection-task-specific deep-learning method to improve the quality of sparse-view myocardial perfusion SPECT images](https://mlsanity.com/paper/2504.16171/) (2025)
- [Hallucinations in medical devices](https://mlsanity.com/paper/2508.14118/) (2025)
- [Image Quality in the Era of Artificial Intelligence](https://mlsanity.com/paper/2602.09347/) (2026)

## Abstract (arXiv)

Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed by use of traditional measures of image quality (IQ) that are commonly employed in the field of computer vision. However, the impact of these methods on objective measures of image quality that are relevant to medical imaging tasks remains largely unexplored. In this study, we investigate the impact of DL-SR methods on binary signal detection performance. Two popular DL-SR methods, the super-resolution convolutional neural network (SRCNN) and the super-resolution generative adversarial network (SRGAN), were trained by use of simulated medical image data. Binary signal-known-exactly with background-known-statistically (SKE/BKS) and signal-known-statistically with background-known-statistically (SKS/BKS) detection tasks were formulated. Numerical observers, which included a neural network-approximated ideal observer and common linear numerical observers, were employed to assess the impact of DL-SR on task performance. The impact of the complexity of the DL-SR network architectures on task-performance was quantified. In addition, the utility of DL-SR for improving the task-performance of sub-optimal observers was investigated. Our numerical experiments confirmed that, as expected, DL-SR could improve traditional measures of IQ. However, for many of the study designs considered, the DL-SR methods provided little or no improvement in task performance and could even degrade it. It was observed that DL-SR could improve the task-performance of sub-optimal observers under certain conditions. The presented study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.