Image Imputation

5 papers with code • 0 benchmarks • 0 datasets

Image imputation is the task of creating plausible images from low-resolution images or images with missing data.

( Image credit: NASA )

Most implemented papers

Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces

boschresearch/continuous-recurrent-units 17 May 2019

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.

Medical Image Imputation from Image Collections

adalca/papago 17 Aug 2018

We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing.

Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN

jongcye/CollaGAN_MRI 10 May 2019

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.

Reconstructing Video from Interferometric Measurements of Time-Varying Sources

achael/eht-imaging 3 Nov 2017

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.

CollaGAN : Collaborative GAN for Missing Image Data Imputation

Superminionsfy/Personalized-Data-Generation-using-GAN 28 Jan 2019

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.