Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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://bitbucket.org/jingweisong/uncertainty-estimation-for-hyper-spectral-image-denoising
공식 코드https://codeberg.org/fuchsfa/foundation-models-seismic-processing
공식 코드Foundation Models For Seismic Data Processing: An Extensive Review
https://github.com/0v/esim-ad
공식 코드Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising
https://github.com/1202kbs/dmcmc
공식 코드Denoising MCMC for Accelerating Diffusion-Based Generative Models
https://github.com/1202kbs/gctm
공식 코드Generalized Consistency Trajectory Models for Image Manipulation
https://github.com/12138-chr/mtair
공식 코드Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation
https://github.com/1429904852/ldf
공식 코드Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection
https://github.com/42shawn/ptq4dm
공식 코드https://github.com/491506870/PRIDNet
공식 코드https://github.com/505030475/ExtremeLowLight
공식 코드Learning an Adaptive Model for Extreme Low-light Raw Image Processing
https://github.com/6tonystark6/scoreadv
공식 코드ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models
https://github.com/74587887/dpps_code
공식 코드https://github.com/838143396/3Diffusion
공식 코드https://github.com/90n9-yu/PT-WGAN
공식 코드https://github.com/A4Bio/DiffSDS_open
공식 코드https://github.com/AdeeshKolluru/AdsorbDiff
공식 코드AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion
https://github.com/AdrienC21/CCSD
공식 코드Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential Equations
https://github.com/AlbertChen1991/nEM
공식 코드https://github.com/AlbertOh90/Soft-VQ-VAE
공식 코드https://github.com/Alejandro-Galan/Global-PC-Large-Transformations-Registration
공식 코드Global Point Cloud Registration Network for Large Transformations
https://github.com/Alescontrela/score_matching_rl
공식 코드Learning a Diffusion Model Policy from Rewards via Q-Score Matching
https://github.com/AlexiaJM/AdversarialConsistentScoreMatching
공식 코드Adversarial score matching and improved sampling for image generation
https://github.com/AliRafiee7/SeConvNet
공식 코드https://github.com/Amirhosein2c/MIP-DDPM
공식 코드https://github.com/AmmarMian/huber_mm_framework
공식 코드https://github.com/AndyShih12/paradigms
공식 코드https://github.com/Arefeen06088/IBI_Tiramisu
공식 코드Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
https://github.com/Arnontu/DeepAudioWaveformPrior
공식 코드https://github.com/Ascend-Research/CascadedGaze
공식 코드CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
https://github.com/Ascend-Research/Turtle
공식 코드Learning Truncated Causal History Model for Video Restoration
https://github.com/AtticusXu/B-Net2
공식 코드Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization
https://github.com/Audio-WestlakeU/CleanMel
공식 코드CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
https://github.com/BBillot/SynthSeg
공식 코드Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining
https://github.com/BIGWangYuDong/UWEnhancement
공식 코드UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space
https://github.com/Bai-YT/ConsistencyTTA
공식 코드ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.