Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/modeltc/tfmq-dm
공식 코드TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models
https://github.com/mogvision/adl
공식 코드https://github.com/mohendra/DIH
공식 코드Machine learning based lens-free imaging technique for field-portable cytometry
https://github.com/mohsenzand/dinof
공식 코드https://github.com/moonbow721/dposer
공식 코드https://github.com/morefre/decoupled-data-consistency-with-diffusion-purification-for-image-restoration
공식 코드Decoupled Data Consistency with Diffusion Purification for Image Restoration
https://github.com/morningmoni/LCGen-eval
공식 코드https://github.com/morningmoni/ede
공식 코드https://github.com/mosaf/res-srdiff
공식 코드https://github.com/motong-ai-studio/textpromptir
공식 코드https://github.com/mrakotosaon/pointcleannet
공식 코드PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
https://github.com/mrayyans/flips
공식 코드https://github.com/mrbled/icicp_2023_wiener
공식 코드https://github.com/mrfanuel/denoising-modulo-samples-local-polynomial-estimator
공식 코드Recovering Hölder smooth functions from noisy modulo samples
https://github.com/mrluin/textualdegremoval
공식 코드Improving Image Restoration through Removing Degradations in Textual Representations
https://github.com/mrzzy2021/styledmotionsynthesis
공식 코드Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models
https://github.com/muelea/buddi
공식 코드Generative Proxemics: A Prior for 3D Social Interaction from Images
https://github.com/mueller-franzes/medfusion
공식 코드https://github.com/muhangtian/timediff
공식 코드https://github.com/multimodallearning/denoised_mt_pcd_reg
공식 코드A denoised Mean Teacher for domain adaptive point cloud registration
https://github.com/mutonix/better_masking
공식 코드Learning Better Masking for Better Language Model Pre-training
https://github.com/muzishen/imagdressing
공식 코드https://github.com/mv-lab/AISP
공식 코드Model-Based Image Signal Processors via Learnable Dictionaries
https://github.com/mv-lab/InstructIR
공식 코드InstructIR: High-Quality Image Restoration Following Human Instructions
https://github.com/myuli/sert
공식 코드Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising
https://github.com/myuli/sst
공식 코드Spatial-Spectral Transformer for Hyperspectral Image Denoising
https://github.com/nPIML-team/nPIML
공식 코드Noise-aware Physics-informed Machine Learning for Robust PDE Discovery
https://github.com/nachifur/rddm
공식 코드https://github.com/nadja1611/self2seg
공식 코드Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising
https://github.com/nagejacob/flornn
공식 코드Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones
https://github.com/nagejacob/spatiallyadaptivessid
공식 코드Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
https://github.com/nagejacob/tbsn
공식 코드Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising
https://github.com/nalinimsingh/interlacer
공식 코드Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis
https://github.com/namjoonsuh/timeautodiff
공식 코드TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing
https://github.com/nandahkrishna/RNNReactivation
공식 코드Sufficient conditions for offline reactivation in recurrent neural networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.