About
Image imputation is the task of creating plausible images from low-resolution images or images with missing data.
( Image credit: NASA )
Benchmarks
Greatest papers with code
Reconstructing Video from Interferometric Measurements of Time-Varying Sources
Most recently, the Event Horizon Telescope (EHT) has extended VLBI to short millimeter wavelengths with a goal of achieving angular resolution sufficient for imaging the event horizons of nearby supermassive black holes.
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
Thanks to the recent success of generative adversarial network (GAN) for image synthesis, there are many exciting GAN approaches that successfully synthesize MR image contrast from other images with different contrasts.
Medical Image Imputation from Image Collections
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing.
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
• Salazar-99/Recurrent-Kalman-Networks
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In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approximate inference techniques such as variational inference which makes learning more complex and often less scalable due to approximation errors.
IMAGE IMPUTATION IMPUTATION TIME SERIES VARIATIONAL INFERENCE
CollaGAN : Collaborative GAN for Missing Image Data Imputation
• Superminionsfy/Personalized-Data-Generation-using-GAN
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In many applications requiring multiple inputs to obtain a desired output, if any of the input data is missing, it often introduces large amounts of bias.