Papers with Code 아카이브

Denoising

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하위 태스크: 6
평가표: 6
보존된 평가 기록: 15

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

<span style="color:grey; opacity: 0.6">( Image credit: Beyond a Gaussian Denoiser )</span>

코드

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

https://github.com/zrealli/TIGIC

공식 코드

Tuning-Free Image Customization with Image and Text Guidance

https://github.com/zrgcityu/graph-involved-frame

공식 코드

Data-Adaptive Graph Framelets with Generalized Vanishing Moments for Graph Signal Processing

https://github.com/zsc15/HPPP

공식 코드

HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image Restoration

https://github.com/zsyOAOA/DANet

공식 코드

Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation

https://github.com/zsyOAOA/VDNet

공식 코드

Variational Denoising Network: Toward Blind Noise Modeling and Removal

https://github.com/zsyOAOA/VIRNet

공식 코드

Deep Variational Network Toward Blind Image Restoration

https://github.com/zubaerimran/SSWL-IDN

공식 코드

Window-Level is a Strong Denoising Surrogate

https://github.com/zwang78/moediff-sr

공식 코드

MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution

https://github.com/zwkatgithub/HGL

공식 코드

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

https://github.com/zwxu064/MPLayers

공식 코드

Fast and Differentiable Message Passing on Pairwise Markov Random Fields

https://github.com/zx-pan/mdm

공식 코드

Masked Diffusion as Self-supervised Representation Learner

https://github.com/zy2219105/ct-mamba

공식 코드

CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising

https://github.com/zylipku/IterativeImaging

공식 코드

Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Boostrapping

https://github.com/zymvszym/ExactDreamer

공식 코드

ExactDreamer: High-Fidelity Text-to-3D Content Creation via Exact Score Matching

https://github.com/zyxelsa/InST

공식 코드

Inversion-Based Style Transfer with Diffusion Models

https://github.com/zzmeei/adaptivegcl

공식 코드

Adaptive Graph Contrastive Learning for Recommendation

https://gitlab.cg.tuwien.ac.at/srasoulzadeh/archcomplete.git

공식 코드

ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based Upsampling

https://gitlab.com/UM-CDS/low-dose-ct-denoising

공식 코드

Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study

https://gitlab.com/a_floquet/automatic-tuning-of-denoising-algorithms-parameters-without-ground-truth

공식 코드

Automatic Tuning of Denoising Algorithms Parameters Without Ground Truth

https://gitlab.com/cian.unibas.ch/diffusion-anomaly

공식 코드

Diffusion Models for Medical Anomaly Detection

https://gitlab.com/elie-chedemail/dp-graph-denoising

공식 코드

Large Graph Signal Denoising with Application to Differential Privacy

https://gitlab.com/trougnouf/rawnind_jddc

공식 코드

Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset

https://gitlab.telecom-paris.fr/RING/MERLIN

공식 코드

As if by magic: self-supervised training of deep despeckling networks with MERLIN

https://gitlab.telecom-paris.fr/RING/SAR-CNN

공식 코드

SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy

https://gitlab.telecom-paris.fr/RING/SAR2SAR

공식 코드

SAR2SAR: a semi-supervised despeckling algorithm for SAR images

https://huggingface.co/spaces/PaddlePaddle/ERNIE-ViLG

공식 코드

ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts

https://github.com/12dmodel/camera_sim

Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation

https://github.com/2023-MindSpore-1/ms-code-141

Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

https://github.com/2023-MindSpore-1/ms-code-149

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/2023-MindSpore-1/ms-code-18/tree/main/llnet

LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement

https://github.com/2023-MindSpore-1/ms-code-211/tree/main/DnCNN

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceQualityAssessment

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

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HiFaceGAN

HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/IPT

Pre-Trained Image Processing Transformer

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/IndexNet

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN

Residual Dense Network for Image Restoration

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