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/mbkiss/2detectcodes

공식 코드

2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

https://github.com/mdswyz/SISN-Face-Hallucination

공식 코드

Face Hallucination via Split-Attention in Split-Attention Network

https://github.com/medarc-ai/fmri-reconstruction-nsd

공식 코드

Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors

https://github.com/meghabyte/bottleneck

공식 코드

Policy Learning with a Language Bottleneck

https://github.com/megvii-research/dcls-sr

공식 코드

Deep Constrained Least Squares for Blind Image Super-Resolution

https://github.com/meijitian/spener

공식 코드

Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction

https://github.com/mengjieshi/learning-based-sos-correction-us-pa

공식 코드

Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imaging

https://github.com/mevanekanayake/cl-mri

공식 코드

CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction

https://github.com/mggonza/bfsnnoai

공식 코드

Combining band-frequency separation and deep neural networks for optoacoustic imaging

https://github.com/mgolbabaee/CoverBLIP

공식 코드

CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction

https://github.com/mgolbabaee/LRTV-MRFResnet-for-MRFingerprinting

공식 코드

Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders

https://github.com/mgolbabaee/SGB-Lasso-for-partial-volume-quantitative-MRI

공식 코드

An off-the-grid approach to multi-compartment magnetic resonance fingerprinting

https://github.com/microsoft/fastmri-plus

공식 코드

fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data

https://github.com/microsoft/i-code

공식 코드

Unifying Vision, Text, and Layout for Universal Document Processing

https://github.com/microsoft/splineposenc

공식 코드

Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields

https://github.com/microsoft/udop

공식 코드

Unifying Vision, Text, and Layout for Universal Document Processing

https://github.com/midas-tum/a-liknet

공식 코드

Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging

https://github.com/midas-tum/ssfl-recon

공식 코드

Self-supervised feature learning for cardiac Cine MR image reconstruction

https://github.com/migamic/pixinwav2

공식 코드

Towards Robust Image-in-Audio Deep Steganography

https://github.com/mikgroup/k-band

공식 코드

K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets

https://github.com/mikgroup/phase_cycling

공식 코드

General Phase Regularized Reconstruction using Phase Cycling

https://github.com/mikgroup/subtle_data_crimes

공식 코드

Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results

https://github.com/mikgroup/subtle_inverse_crimes

공식 코드

Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results

https://github.com/mikgroup/ufloss

공식 코드

High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss

https://github.com/mindflow-institue/awesome-implicit-neural-representations-in-medical-imaging

공식 코드

Implicit Neural Representation in Medical Imaging: A Comparative Survey

https://github.com/mingtian99/esdnet

공식 코드

Learning A Spiking Neural Network for Efficient Image Deraining

https://github.com/mit-han-lab/hart

공식 코드

HART: Efficient Visual Generation with Hybrid Autoregressive Transformer

https://github.com/mivrc/eamri

공식 코드

Fast MRI Reconstruction via Edge Attention

https://github.com/mli-lab/cinemri

공식 코드

Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction

https://github.com/mli-lab/imaging_mlps

공식 코드

Image-to-Image MLP-mixer for Image Reconstruction

https://github.com/mli-lab/mri_data_diversity

공식 코드

Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data

https://github.com/mli-lab/scaling_laws_for_deep_learning_based_image_reconstruction

공식 코드

Scaling Laws For Deep Learning Based Image Reconstruction

https://github.com/mli-lab/ttt_for_deep_learning_cs

공식 코드

Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing

https://github.com/moepien/sliced_ot_for_gmms

공식 코드

Slicing the Gaussian Mixture Wasserstein Distance

https://github.com/mohammad-adiban/Video-Prediction

공식 코드

Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation

https://github.com/mohammedsalah98/e_calib

공식 코드

E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras

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