{"list":"other","total":76,"rows":[{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2206.00364","title":"Elucidating the Design Space of Diffusion-Based Generative Models","created":"2022-06-01","cited_by_count":3045},{"id":"1606.03498","title":"Improved Techniques for Training GANs","created":"2016-06-10","cited_by_count":2970},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"2209.14687","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","created":"2022-09-29","cited_by_count":1416},{"id":"1608.03981","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","created":"2016-08-13","cited_by_count":1358},{"id":"1904.12848","title":"Unsupervised Data Augmentation for Consistency Training","created":"2019-04-29","cited_by_count":1060},{"id":"1711.06077","title":"The Perception-Distortion Tradeoff","created":"2017-11-16","cited_by_count":587},{"id":"1811.08839","title":"fastMRI: An Open Dataset and Benchmarks for Accelerated MRI","created":"2018-11-21","cited_by_count":470},{"id":"2008.13751","title":"Plug-and-Play Image Restoration with Deep Denoiser Prior","created":"2020-08-31","cited_by_count":470},{"id":"1611.03679","title":"Deep Convolutional Neural Network for Inverse Problems in Imaging","created":"2016-11-11","cited_by_count":433},{"id":"1712.02862","title":"MoDL: Model Based Deep Learning Architecture for Inverse Problems","created":"2017-12-07","cited_by_count":387},{"id":"1704.00447","title":"Learning a Variational Network for Reconstruction of Accelerated MRI Data","created":"2017-04-03","cited_by_count":342},{"id":"2110.05243","title":"Score-based diffusion models for accelerated MRI","created":"2021-10-08","cited_by_count":311},{"id":"2005.06001","title":"Deep Learning Techniques for Inverse Problems in Imaging","created":"2020-05-12","cited_by_count":291},{"id":"2108.01368","title":"Robust Compressed Sensing MRI with Deep Generative Priors","created":"2021-08-03","cited_by_count":286},{"id":"1711.10337","title":"Are GANs Created Equal? A Large-Scale Study","created":"2017-11-28","cited_by_count":267},{"id":"2004.06688","title":"End-to-End Variational Networks for Accelerated MRI Reconstruction","created":"2020-04-14","cited_by_count":202},{"id":"2410.00083","title":"A Survey on Diffusion Models for Inverse Problems","created":"2024-09-30","cited_by_count":180},{"id":"2110.09327","title":"Self-Supervised Representation Learning: Introduction, Advances and Challenges","created":"2021-10-18","cited_by_count":148},{"id":"2108.10991","title":"NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction","created":"2021-08-24","cited_by_count":146},{"id":"1910.11908","title":"Noisier2Noise: Learning to Denoise from Unpaired Noisy Data","created":"2019-10-25","cited_by_count":126},{"id":"2305.19256","title":"Ambient Diffusion: Learning Clean Distributions from Corrupted Data","created":"2023-05-30","cited_by_count":111},{"id":"1712.01751","title":"Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction","created":"2017-12-05","cited_by_count":105},{"id":"2103.14756","title":"Equivariant Imaging: Learning Beyond the Range Space","created":"2021-03-26","cited_by_count":83},{"id":"2105.08059","title":"Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers","created":"2021-05-15","cited_by_count":77},{"id":"1912.05854","title":"RARE: Image Reconstruction using Deep Priors Learned without Ground Truth","created":"2019-12-12","cited_by_count":64},{"id":"2111.12855","title":"Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements","created":"2021-11-25","cited_by_count":56},{"id":"2102.07737","title":"Zero-Shot Self-Supervised Learning for MRI Reconstruction","created":"2021-02-15","cited_by_count":54},{"id":"1805.10531","title":"Unsupervised Learning with Stein's Unbiased Risk Estimator","created":"2018-05-26","cited_by_count":47},{"id":"2203.06823","title":"SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation","created":"2022-03-14","cited_by_count":44},{"id":"2305.13128","title":"GSURE-Based Diffusion Model Training with Corrupted Data","created":"2023-05-22","cited_by_count":40},{"id":"2402.15865","title":"HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models","created":"2024-02-24","cited_by_count":39},{"id":"2112.12744","title":"AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis","created":"2021-12-23","cited_by_count":38},{"id":"2204.07204","title":"Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing","created":"2022-04-14","cited_by_count":37},{"id":"1902.00301","title":"Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution","created":"2019-02-01","cited_by_count":36},{"id":"2209.01725","title":"Imaging with Equivariant Deep Learning","created":"2022-09-05","cited_by_count":35},{"id":"2111.09708","title":"A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration","created":"2021-11-18","cited_by_count":34},{"id":"2106.03538","title":"End-to-end reconstruction meets data-driven regularization for inverse problems","created":"2021-06-07","cited_by_count":32},{"id":"2403.08728","title":"Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data","created":"2024-03-13","cited_by_count":32},{"id":"2205.10278","title":"A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise","created":"2022-05-20","cited_by_count":30},{"id":"2008.12967","title":"Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN","created":"2020-08-29","cited_by_count":28},{"id":"2109.12502","title":"Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework","created":"2021-09-26","cited_by_count":26},{"id":"2106.14947","title":"Data augmentation for deep learning based accelerated MRI reconstruction with limited data","created":"2021-06-28","cited_by_count":20},{"id":"2201.12151","title":"Unsupervised Learning From Incomplete Measurements for Inverse Problems","created":"2022-01-28","cited_by_count":20},{"id":"2102.00034","title":"Dynamic imaging using a deep generative SToRM (Gen-SToRM) model","created":"2021-01-29","cited_by_count":17},{"id":"2105.08040","title":"Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement","created":"2021-05-17","cited_by_count":17},{"id":"2405.19097","title":"A study of why we need to reassess full reference image quality assessment with medical images","created":"2024-05-29","cited_by_count":17},{"id":"2111.02549","title":"VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction","created":"2021-11-03","cited_by_count":16},{"id":"2409.01985","title":"UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate","created":"2024-09-03","cited_by_count":16},{"id":"1806.00961","title":"Training deep learning based image denoisers from undersampled measurements without ground truth and without image prior","created":"2018-06-04","cited_by_count":15},{"id":"1910.07048","title":"Wasserstein GANs for MR Imaging: from Paired to Unpaired Training","created":"2019-10-15","cited_by_count":14},{"id":"2007.14979","title":"Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data","created":"2020-07-29","cited_by_count":14},{"id":"2010.10631","title":"ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms","created":"2020-10-20","cited_by_count":14},{"id":"2302.09244","title":"Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction","created":"2023-02-18","cited_by_count":13},{"id":"1909.13482","title":"Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions","created":"2019-09-30","cited_by_count":12},{"id":"1910.13110","title":"Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data","created":"2019-10-29","cited_by_count":10},{"id":"2210.01696","title":"Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDU","created":"2022-10-04","cited_by_count":10},{"id":"2210.02584","title":"SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction","created":"2022-10-05","cited_by_count":10},{"id":"2107.05533","title":"Deformation-Compensated Learning for Image Reconstruction without Ground Truth","created":"2021-07-12","cited_by_count":9},{"id":"1905.09325","title":"Self-supervised learning of inverse problem solvers in medical imaging","created":"2019-05-22","cited_by_count":8},{"id":"1906.05775","title":"Training Image Estimators without Image Ground-Truth","created":"2019-06-13","cited_by_count":8},{"id":"2202.01494","title":"PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging","created":"2022-02-03","cited_by_count":8},{"id":"2205.03519","title":"Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising","created":"2022-05-07","cited_by_count":8},{"id":"2305.19079","title":"Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods","created":"2023-05-30","cited_by_count":8},{"id":"2402.16921","title":"Sparse2Inverse: Self-supervised inversion of sparse-view CT data","created":"2024-02-26","cited_by_count":8},{"id":"2201.12535","title":"Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI","created":"2022-01-29","cited_by_count":6},{"id":"2308.02958","title":"K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets","created":"2023-08-05","cited_by_count":6},{"id":"2412.08350","title":"Benchmarking learned algorithms for computed tomography image reconstruction tasks","created":"2024-12-11","cited_by_count":6},{"id":"2403.09327","title":"Perspective-Equivariance for Unsupervised Imaging with Camera Geometry","created":"2024-03-14","cited_by_count":5},{"id":"2009.13986","title":"Deep Image Reconstruction using Unregistered Measurements without Groundtruth","created":"2020-09-29","cited_by_count":3},{"id":"2304.12890","title":"MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data","created":"2023-04-25","cited_by_count":3},{"id":"2410.08646","title":"Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal Equivariance","created":"2024-10-11","cited_by_count":3},{"id":"2110.00075","title":"Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning","created":"2021-09-30","cited_by_count":2},{"id":"2411.05883","title":"Benchmarking 3D multi-coil NC-PDNet MRI reconstruction","created":"2024-11-08","cited_by_count":2}],"next_cursor":null}