# A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement

arXiv: https://arxiv.org/abs/2106.15575 · HTML: https://mlsanity.com/paper/2106.15575/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Super-Resolution with Deep Convolutional Sufficient Statistics](https://mlsanity.com/paper/1511.05666/) (2015)
- [Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery](https://mlsanity.com/paper/1705.08260/) (2017)
- [Robust Super-Resolution GAN, with Manifold-based and Perception Loss](https://mlsanity.com/paper/1903.06920/) (2019)
- [Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks](https://mlsanity.com/paper/1812.11302/) (2018)
- [Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints](https://mlsanity.com/paper/1610.01066/) (2016)

## Built on by (most cited first, 11 of 11)

- [BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks](https://mlsanity.com/paper/2207.06873/) (2022)
- [Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data](https://mlsanity.com/paper/2107.09892/) (2021)
- [Robustness via Uncertainty-aware Cycle Consistency](https://mlsanity.com/paper/2110.12467/) (2021)
- [Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement](https://mlsanity.com/paper/2110.03343/) (2021)
- [Deep Learning Generates Synthetic Cancer Histology for Explainability and Education](https://mlsanity.com/paper/2211.06522/) (2022)
- [USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution](https://mlsanity.com/paper/2305.17520/) (2023)
- [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024)
- [Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution](https://mlsanity.com/paper/2406.18310/) (2024)
- [An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution](https://mlsanity.com/paper/2401.15613/) (2024)
- [Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosis](https://mlsanity.com/paper/2505.23353/) (2025)
- [Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models](https://mlsanity.com/paper/2211.06250/) (2022)

## Abstract (arXiv)

Deep neural networks for image quality enhancement typically need large quantities of highly-curated training data comprising pairs of low-quality images and their corresponding high-quality images. While high-quality image acquisition is typically expensive and time-consuming, medium-quality images are faster to acquire, at lower equipment costs, and available in larger quantities. Thus, we propose a novel generative adversarial network (GAN) that can leverage training data at multiple levels of quality (e.g., high and medium quality) to improve performance while limiting costs of data curation. We apply our mixed-supervision GAN to (i) super-resolve histopathology images and (ii) enhance laparoscopy images by combining super-resolution and surgical smoke removal. Results on large clinical and pre-clinical datasets show the benefits of our mixed-supervision GAN over the state of the art.