Robust Super-Resolution GAN, with Manifold-based and Perception Loss
This addresses the challenge of robust super-resolution for clinical imaging, where data quality issues are common, though it appears incremental with specific enhancements to existing GAN frameworks.
The paper tackles the problem of super-resolution in clinical settings where training data often contains corruptions, by proposing novel loss functions that model heavy-tailed non-Gaussian residuals and incorporate manifold-based and perception-based metrics. Results on a large clinical dataset show improvements over state-of-the-art methods.
Super-resolution using deep neural networks typically relies on highly curated training sets that are often unavailable in clinical deployment scenarios. Using loss functions that assume Gaussian-distributed residuals makes the learning sensitive to corruptions in clinical training sets. We propose novel loss functions that are robust to corruptions in training sets by modeling heavy-tailed non-Gaussian distributions on the residuals. We propose a loss based on an autoencoder-based manifold-distance between the super-resolved and high-resolution images, to reproduce realistic textural content in super-resolved images. We propose to learn to super-resolve images to match human perceptions of structure, luminance, and contrast. Results on a large clinical dataset shows the advantages of each of our contributions, where our framework improves over the state of the art.