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/jorgmateos/uformer-google-colab
Uformer: A General U-Shaped Transformer for Image Restoration
https://github.com/jorisweeda/quaternion-convolutional-neural-networks
https://github.com/joseluis1061/neuralnilm
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
https://github.com/juglab/DivNoising
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
https://github.com/juglab/HDN
Interpretable Unsupervised Diversity Denoising and Artefact Removal
https://github.com/juglab/N2V_fiji
https://github.com/juglab/Noise2Noise-with-CSBDeep
https://github.com/juglab/cryocare_t2t
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data
https://github.com/juglab/n2v
https://github.com/juglab/pn2v
Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
https://github.com/jupiterman/Super-Resolution-Images
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/jwliao1209/diffqrcode
https://github.com/kabirdabholkar/lfads-torch
lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems
https://github.com/kakazxz/myops
Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images
https://github.com/kauziishere/lowLightImageEnhancement
https://github.com/kdhRick2222/Exposure-slot
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
https://github.com/keerthan2/RDN-Tensorflow
https://github.com/keonlee9420/DiffGAN-TTS
https://github.com/keonlee9420/DiffSinger
https://github.com/keras-team/keras-cv
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/keras-team/keras-io/blob/master/examples/generative/ddim.py
https://github.com/kfallah/NODE-Denoiser
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/kglore/llnet_color
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
https://github.com/khalida1wwin/Noise2Noise-audio_denoising_without_clean_training_data
Speech Denoising Without Clean Training Data: A Noise2Noise Approach
https://github.com/khushsi/Finetuning_BART_for_FACET_Summarization
https://github.com/kidanger/high-noise-deblurring
https://github.com/kimdanni/DiffMAE
https://github.com/kpandey008/DCGANS
https://github.com/kpandey008/DiffuseVAE
https://github.com/kunalrdeshmukh/End-to-end-compression
An End-to-End Compression Framework Based on Convolutional Neural Networks
https://github.com/kuntojirohan/speechdenoising
https://github.com/kyeongry/Autoencoders
https://github.com/kyutai-labs/moshi
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/labshuhanggu/mia-vsr
Recurrent Video Restoration Transformer with Guided Deformable Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.