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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/yuanfeihuang/condformer

공식 코드

Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer

https://github.com/yuanhy1997/seqdiffuseq

공식 코드

SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers

https://github.com/yuanzhi-zhu/diffpir

공식 코드

Denoising Diffusion Models for Plug-and-Play Image Restoration

https://github.com/yuchen005/nase

공식 코드

Noise-aware Speech Enhancement using Diffusion Probabilistic Model

https://github.com/yuchen005/robustger

공식 코드

Large Language Models are Efficient Learners of Noise-Robust Speech Recognition

https://github.com/yue-zhongqi/diti

공식 코드

Exploring Diffusion Time-steps for Unsupervised Representation Learning

https://github.com/yuhuustc/debias

공식 코드

Unmasking Bias in Diffusion Model Training

https://github.com/yuhuustc/dehazeddpm

공식 코드

High-quality Image Dehazing with Diffusion Model

https://github.com/yulunzhang/RDN

공식 코드

Residual Dense Network for Image Super-Resolution

https://github.com/yulunzhang/RNAN

공식 코드

Residual Non-local Attention Networks for Image Restoration

https://github.com/yunfanlu/rgb-event-isp

공식 코드

RGB-Event ISP: The Dataset and Benchmark

https://github.com/yunhaoyang234/patch-subspace-learning-autoencoder

공식 코드

Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal Processing

https://github.com/yunshuwu/ContrastiveDiffusionLoss

공식 코드

Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training

https://github.com/yushundong/AdaGNN

공식 코드

AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter

https://github.com/yusra-alkendi/ed-kogtl

공식 코드

Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers

https://github.com/yutazhuo/diffusereg

공식 코드

DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image Registration

https://github.com/yuxin-zhang-jasmine/drus-v1

공식 코드

Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models

https://github.com/yuxin-zhang-jasmine/drusvar

공식 코드

Ultrasound Imaging based on the Variance of a Diffusion Restoration Model

https://github.com/yuxin-zhang-jasmine/ius2024_diffusion

공식 코드

Ultrasound Image Enhancement with the Variance of Diffusion Models

https://github.com/yuxumin/PoinTr

공식 코드

AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers

https://github.com/yuyang-shi/generalized-diffusion

공식 코드

From Denoising Diffusions to Denoising Markov Models

https://github.com/yvanyin/goalflow

공식 코드

GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving

https://github.com/yxltya/pointdgmamba

공식 코드

PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model

https://github.com/yxlu-0102/MP-SENet

공식 코드

MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra

https://github.com/yyqqss09/ldct_denoising

공식 코드

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

https://github.com/yyt-2378/automat

공식 코드

AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use

https://github.com/yzhhoward/hmnet

공식 코드

Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting

https://github.com/yzhouas/PD-Denoising-pytorch

공식 코드

When AWGN-based Denoiser Meets Real Noises

https://github.com/z-bingo/Attention-Mechanism-Enhanced-KPN

공식 코드

Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst Images

https://github.com/z-fabian/dirac-diffusion

공식 코드

DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency

https://github.com/zadid6pretam/TabSeq

공식 코드

TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering

https://github.com/zahraakhlaghi/alda4rec

공식 코드

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation

https://github.com/zahraatashgahi/QuickSelection

공식 코드

Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders

https://github.com/zc-97/dpt

공식 코드

Denoising and Prompt-Tuning for Multi-Behavior Recommendation

https://github.com/zc-97/ssdrec

공식 코드

SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation

https://github.com/zengjianhao/cat-dm

공식 코드

CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model

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