Papers with Code 아카이브

Image Reconstruction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

5

평가표

8

보존된 평가 기록

35

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/utcsilab/motion_score_mri

공식 코드

Accelerated Motion Correction with Deep Generative Diffusion Models

https://github.com/uzh-rpg/e2calib

공식 코드

How to Calibrate Your Event Camera

https://github.com/vegarant/Invfool

공식 코드

On instabilities of deep learning in image reconstruction - Does AI come at a cost?

https://github.com/vila-lab/i-mae

공식 코드

i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

https://github.com/vios-s/CMRxRECON_Challenge_EDIPO

공식 코드

Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement

https://github.com/vios-s/Inference_Denoising_MRI_Recon

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Inference Stage Denoising for Undersampled MRI Reconstruction

https://github.com/vios-s/multimodal_segmentation

공식 코드

Disentangle, align and fuse for multimodal and semi-supervised image segmentation

https://github.com/vision-learning-acceleration-lab/i-mae

공식 코드

i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

https://github.com/vita-group/chasing-better-dips

공식 코드

Chasing Better Deep Image Priors between Over- and Under-parameterization

https://github.com/vita-group/trap-and-replace-backdoor-defense

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Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork

https://github.com/vlar-group/unmore

공식 코드

unMORE: Unsupervised Multi-Object Segmentation via Center-Boundary Reasoning

https://github.com/vlislab22/Deep-Learning-for-Event-based-Vision

공식 코드

Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks

https://github.com/vlogroup/bigger-isnt-always-better

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Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction

https://github.com/vmboehm/pae-ablation

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Probabilistic Autoencoder

https://github.com/vonclites/cogan

공식 코드

Synthesis-Guided Feature Learning for Cross-Spectral Periocular Recognition

https://github.com/vpronina/DeepWienerRestoration

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Microscopy Image Restoration with Deep Wiener-Kolmogorov filters

https://github.com/vt-vl-lab/Guided-pix2pix

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Guided Image-to-Image Translation with Bi-Directional Feature Transformation

https://github.com/waleedgondal/Texture-based-Super-Resolution-Network

공식 코드

The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution

https://github.com/wanderinrain/xray2ct

공식 코드

CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapy

https://github.com/wangwei-cmd/CT-image-reconstruction

공식 코드

A deep network for sinogram and CT image reconstruction

https://github.com/wangwei-cmd/limited-angle-CT-reconstruction

공식 코드

A model-guided deep network for limited-angle computed tomography

https://github.com/wangyunpengbio/x-recon

공식 코드

X-Recon: Learning-based Patient-specific High-Resolution CT Reconstruction from Orthogonal X-Ray Images

https://github.com/wangziblake/pisf

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One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

https://github.com/warvito/generative_monai

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Generative AI for Medical Imaging: extending the MONAI Framework

https://github.com/wdika/atommic

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Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction

https://github.com/wdika/mridc

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Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine

https://github.com/wenqihuang/LS-Net-Dynamic-MRI

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Deep Low-rank plus Sparse Network for Dynamic MR Imaging

https://github.com/wjgancn/ptychodv

공식 코드

PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction

https://github.com/wl082013/ESIM_dataset

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EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning

https://github.com/woxuankai/spatialalignmentnetwork

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Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network

https://github.com/wu-cvgl/scinerf

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SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image

https://github.com/wumengshenyou/gso

공식 코드

Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems

https://github.com/wustl-cig/Cooridnate-based-Internal-Learning

공식 코드

CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems

https://github.com/wustl-cig/DeCoLearn

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Deformation-Compensated Learning for Image Reconstruction without Ground Truth

https://github.com/wustl-cig/RARE

공식 코드

RARE: Image Reconstruction using Deep Priors Learned without Ground Truth

https://github.com/wustl-cig/ScatteringDecoder

공식 코드

Efficient and accurate inversion of multiple scattering with deep learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.