Image Imputation
6 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 )
Benchmarks
These leaderboards are used to track progress in Image Imputation
Most implemented papers
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
boschresearch/continuous-recurrent-units
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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.
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.
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.
Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis
To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis.
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.
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.
