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/earthspecies/audio-embeddings
https://github.com/edward-reehorst/On_RED
Regularization by Denoising: Clarifications and New Interpretations
https://github.com/egkang-luis/burstm
Burstormer: Burst Image Restoration and Enhancement Transformer
https://github.com/elras/SoftsegmentNeurons
Modular proximal optimization for multidimensional total-variation regularization
https://github.com/emanueledalsasso/SAR2SAR
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
https://github.com/envision-research/lotus
Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
https://github.com/eoinoreilly30/seeing-in-the-dark
https://github.com/era-dorta/tf_mvg
https://github.com/ericl122333/patchdiffusion-pytorch
https://github.com/ericoldgren/kex---ct-reconstruction
https://github.com/ethz-spylab/diffusion_denoised_smoothing
https://github.com/evtaktasheva/dependency_extraction
Multilingual Denoising Pre-training for Neural Machine Translation
https://github.com/explainingai-code/DDPM-Pytorch
https://github.com/explainingai-code/StableDiffusion-PyTorch
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/exploration-lab/iitk-semeval-2024-task-1
https://github.com/fabidicia/DANAE
DANAE: a denoising autoencoder for underwater attitude estimation
https://github.com/facebookresearch/DomainBed
Domain Generalization for Object Recognition with Multi-task Autoencoders
https://github.com/facebookresearch/GENRE
https://github.com/facebookresearch/bart_ls
https://github.com/facebookresearch/flowmm
https://github.com/facebookresearch/w2ot
https://github.com/fanbeatsman/tensorflow-quick-start-image-processing-focus-
https://github.com/farrowlab/Neuron-Softsegmentation
Modular proximal optimization for multidimensional total-variation regularization
https://github.com/fashn-AI/tryondiffusion
Simple diffusion: End-to-end diffusion for high resolution images
https://github.com/fhshen2022/prunerepaint
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/finnbehrendt/ensembled-ssim-for-unsupervised-anomaly-detection
https://github.com/finspire13/diffact
https://github.com/flyingdoog/PTDNet
Learning to Drop: Robust Graph Neural Network via Topological Denoising
https://github.com/foolish0/n2n
https://github.com/fperdigon/DeepFilter
https://github.com/fpramunno/InDI-implementation
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
https://github.com/francois-rozet/mmps-benchmark
Denoising Diffusion Models for Plug-and-Play Image Restoration
https://github.com/francoisgermain/SpeechDenoisingWithDeepFeatureLosses
https://github.com/futurexiang/soda
SODA: Bottleneck Diffusion Models for Representation Learning
https://github.com/fvl2020/iccv-2023-mb-taylorformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.