Papers with Code 아카이브
Denoising
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- 평가표
- 6
- 보존된 평가 기록
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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/safwankdb/Deep-Image-Prior
https://github.com/saihiel/news_headlines
Educating Text Autoencoders: Latent Representation Guidance via Denoising
https://github.com/sailor-z/clnet
https://github.com/saiteja2901/NVDIA_AI_NoiseRemoval
https://github.com/sak-h/pytorch-Denoising-Diffusion-Probabilistic-Models
https://github.com/samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches
https://github.com/samb-t/unleashing-transformers
https://github.com/sami-automatic/SADNet_Replication
https://github.com/samyakjain0112/Image-Denoising-in-extreme-low-light
https://github.com/samyakjain0112/Video-denoising_tensorflow-core-api-2.0
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation
https://github.com/sanglee325/cutblur
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
https://github.com/sanster/lama-cleaner
https://github.com/sanweiliti/RoHM
https://github.com/sanyuan-chen/unispeech
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/sarahesl/pubmedclip
Overcoming Data Limitation in Medical Visual Question Answering
https://github.com/saurabh-kataria/PERL-samples
https://github.com/saurabh-kataria/projected-distribution-loss
https://github.com/sayakpaul/maxim-tf
https://github.com/sayannath/Low-Light-Image-Enhancement
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/sayannath/MIRNet-Flutter
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/sdoria/SID
https://github.com/secml-lab-vt/EvolvingThreat-DeepfakeImageDetect
MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Network
https://github.com/seervideodiffusion/SeerVideoLDM
Seer: Language Instructed Video Prediction with Latent Diffusion Models
https://github.com/senmaoy/RAT-Diffusion
https://github.com/sentient-engineering/agent-q
Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems
https://github.com/ser-art/RAE-vs-AE
https://github.com/setsunil/dsdnet
Improving Image Restoration by Revisiting Global Information Aggregation
https://github.com/sevenysw/python_segy
https://github.com/shangqigao/RONet
Rank-One Network: An Effective Framework for Image Restoration
https://github.com/shaonianruntu/MWCNN
https://github.com/sharonwang1/seg_denoising
Segmentation-Aware Image Denoising without Knowing True Segmentation
https://github.com/shibuiwilliam/DeepLearningDenoise
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/shihaozhaozsh/lavi-bridge
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
https://github.com/shijx12/kqapro_baselines
https://github.com/shilin-lu/mace
Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.