Papers with Code 아카이브

Denoising

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/nanmehta/gmtnet

공식 코드

Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement

https://github.com/naraysa/D2-Net

공식 코드

D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations

https://github.com/nargesnemati/mccl

공식 코드

Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation

https://github.com/nasa/wind-generative-modeling

공식 코드

Generative Modeling of Microweather Wind Velocities for Urban Air Mobility

https://github.com/naszilla/naszilla

공식 코드

Exploring the Loss Landscape in Neural Architecture Search

https://github.com/naver-ai/Visual-Style-Prompting

공식 코드

Visual Style Prompting with Swapping Self-Attention

https://github.com/nayelbettache/bmlr

공식 코드

Bivariate Matrix-valued Linear Regression (BMLR): Finite-sample performance under Identifiability and Sparsity Assumptions

https://github.com/ncclabsustech/EEGdenoiseNet

공식 코드

EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising

https://github.com/ncclabsustech/Single-Channel-EEG-Denoise

공식 코드

EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising

https://github.com/ncclabsustech/deepseparator

공식 코드

Embedding Decomposition for Artifacts Removal in EEG Signals

https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization

공식 코드

Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

https://github.com/nemo-1024/adrec

공식 코드

Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach

https://github.com/neogeoss/EMBED_Mammo_Models

공식 코드

Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography

https://github.com/neon-jing/guider

공식 코드

Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration

https://github.com/networkslab/cdiff

공식 코드

Interacting Diffusion Processes for Event Sequence Forecasting

https://github.com/neuraloperator/fundps

공식 코드

Guided Diffusion Sampling on Function Spaces with Applications to PDEs

https://github.com/nevsnev/fgdvi

공식 코드

Flow-Guided Diffusion for Video Inpainting

https://github.com/newbeeer/stf

공식 코드

Stable Target Field for Reduced Variance Score Estimation in Diffusion Models

https://github.com/ngruver/nos

공식 코드

Protein Design with Guided Discrete Diffusion

https://github.com/ngunnar/latent-linear-dynamics-in-spatiotemporal-medical-data

공식 코드

Unsupervised dynamic modeling of medical image transformation

https://github.com/ngunnar/med-dyn-reg

공식 코드

Unsupervised dynamic modeling of medical image transformation

https://github.com/nguyenkh/NeuralDenoising

공식 코드

Neural-based Noise Filtering from Word Embeddings

https://github.com/niamhbelton/ss-fewsome_disease_severity_knee_osteoarthritis

공식 코드

An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data

https://github.com/nianticlabs/diffusionerf

공식 코드

DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models

https://github.com/nicholasjohnson2020/discretecompressedsensing.jl

공식 코드

Compressed Sensing: A Discrete Optimization Approach

https://github.com/nickeilf/diffusedef

공식 코드

DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative Denoising

https://github.com/nickzhang53/symmetricdiffusers

공식 코드

SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups

https://github.com/nicolas-dufour/plonk

공식 코드

Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation

https://github.com/nicolaus-huang/processpainter

공식 코드

ProcessPainter: Learn Painting Process from Sequence Data

https://github.com/night-chen/dygen

공식 코드

DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling

https://github.com/niika/SIDAR

공식 코드

SIDAR: Synthetic Image Dataset for Alignment & Restoration

https://github.com/nikhilvravi/DukeSR

공식 코드

New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution

https://github.com/nikopj/cdlnet-ojsp

공식 코드

CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning

https://github.com/nikopj/groupcdl-tip

공식 코드

Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning

https://github.com/nilotpal09/superresolutionhep

공식 코드

Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction

https://github.com/ninikolov/summary-denoising

공식 코드

Summary Refinement through Denoising

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