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/cycentum/bert-based-text-generation
https://github.com/cydonia999/Learning_to_See_in_the_Dark_PyTorch
https://github.com/cyhuang-tw/robust-vc
https://github.com/cywang97/unispeech
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/daintlab/ct-denoising
https://github.com/dan8991/Image-coding-perceptual-enhancement-with-diffusion-models
https://github.com/danilka-na/DegreeWork
https://github.com/daspartho/magicmix
https://github.com/dataflowr/notebooks/blob/master/Module18/ddpm_micro_sol.ipynb
https://github.com/david-dunson/GeodesicDistance
Efficient Manifold and Subspace Approximations with Spherelets
https://github.com/david-li0406/read
https://github.com/dawn-lx/causal-videogen
Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing
https://github.com/dawn0815/UniSA
https://github.com/dcmocanu/sparse-evolutionary-artificial-neural-networks
https://github.com/debanjanxy/GNR-652
https://github.com/deepinv/deepinv
Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise
https://github.com/deepvk/metr
Invisible Image Watermarks Are Provably Removable Using Generative AI
https://github.com/deforum/stable-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/deshanyang/abdominal-dir-qa
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/deshanyang/liver-dir-qa
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/dhruvpatel108/GANPriors
https://github.com/dhryougit/afm
https://github.com/diskhkme/VCONV_DAE_TF
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
https://github.com/divamgupta/stable-diffusion-tensorflow
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/dniku/perceptual-gradient-networks
https://github.com/dome272/wuerstchen
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
https://github.com/donghoney0416/DeFT-AN
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech Enhancement
https://github.com/dongyan007/Pretrained-IPT-main-master
https://github.com/dopplerchase/cira-diff
https://github.com/dsl-lab/swingnn
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
https://github.com/dsl-lab/symmetricdiffusers
SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups
https://github.com/dslisleedh/NAFNet-flax
https://github.com/dslisleedh/NAFNet-tensorflow2
https://github.com/dsubuntu/scie
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?
https://github.com/dynamicslab/langevin-regression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.