Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/Build-Week-Spotify-Song-Suggester-5/Data-Science
https://github.com/CLEANit/SRS2021
https://github.com/COMP6248-Reproducability-Challenge/selfsupervised-denoising
https://github.com/CW-Huang/sdeflow-light
https://github.com/ChungHanYang/Image-retoration-based-on-SRresnet
https://github.com/Coldog2333/pytoflow
https://github.com/CompVis/image2video-synthesis-using-cINNs
https://github.com/CorentinBT/Decoder
Learning to compress and search visual data in large-scale systems
https://github.com/CroitoruAlin/Diffusion-Models-in-Vision-A-Survey
https://github.com/DDCM-2025/ddcm-compressed-image-generation
Compressed Image Generation with Denoising Diffusion Codebook Models
https://github.com/DataScienceNigeria/SummAE-from-Google-Brain-and-MIT-CSAIL
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
https://github.com/Design-and-Simulation/CACTI-Simulation
Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging
https://github.com/DiT-3D/DiT-3D
DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation
https://github.com/DivJAth/DeepLearning5922
https://github.com/DmitryUlyanov/deep-image-prior
https://github.com/Dongshengjiang/Denoising-of-3D-magnetic-resonance-images-with-multi-channel-residual-CNN
https://github.com/EDAPINENUT/GNTPP
https://github.com/EmoryMLIP/DynamicBlocks
Deep Neural Networks Motivated by Partial Differential Equations
https://github.com/Ending2015a/toy_gradlogp
https://github.com/EntropicEffect/dendritic_backprop
Dendritic error backpropagation in deep cortical microcircuits
https://github.com/FinnBehrendt/Mahalanobis-Unsupervised-Anomaly-Detection
https://github.com/Francis-Rings/MotionEditor
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/Garoe/tf_mvg
https://github.com/GarrickZ2/Image-Denoising
CycleISP: Real Image Restoration via Improved Data Synthesis
https://github.com/GaryMataev/DeepRED
https://github.com/Gaurav14cs17/Reparameterization-Denoising
Lightweight network towards real-time image denoising on mobile devices
https://github.com/GauriJagatap/invimaging-deeppriors
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
https://github.com/Georg030/MRI-Image-Denoising
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/HDCVLab/MC-Blur-Dataset
https://github.com/HHousen/TransformerSum
https://github.com/HKUNLP/Dream
Structured Denoising Diffusion Models in Discrete State-Spaces
https://github.com/HarryK24/MIDA-pytorch
https://github.com/HilaManor/Generative-deep-features
https://github.com/HosseinHosseini/Semantic-Adversarial-Examples
https://github.com/House-Leo/DnSwin
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.