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Found 9 papers, 6 papers with code
Joint Demosaicing and Denoising with Double Deep Image Priors
Demosaicing and denoising of RAW images are crucial steps in the processing pipeline of modern digital cameras.
Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC
The transparency of each crystal is monitored with a laser monitoring system that tracks changes in the optical properties of the crystals due to radiation from the collision products.
Robust Autoencoders for Collective Corruption Removal
Robust PCA is a standard tool for learning a linear subspace in the presence of sparse corruption or rare outliers.
Deep Random Projector: Accelerated Deep Image Prior
Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data.
Blind Image Deblurring with Unknown Kernel Size and Substantial Noise
Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields.
Early Stopping for Deep Image Prior
In this regard, the majority of DIP works for vision tasks only demonstrates the potential of the models -- reporting the peak performance against the ground truth, but provides no clue about how to operationally obtain near-peak performance without access to the groundtruth.
Self-Validation: Early Stopping for Single-Instance Deep Generative Priors
Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data.
Rethinking Transfer Learning for Medical Image Classification
Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC).
A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals
Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms.
