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/ah7149407/ee193-03_project
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
https://github.com/ai-med/stablepose
HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation
https://github.com/aiff22/PyNET-PyTorch
Replacing Mobile Camera ISP with a Single Deep Learning Model
https://github.com/aimagelab/multimodal-garment-designer
Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing
https://github.com/ajazturki10/Credit-Card-Fraud-Detection-using-Autoencoders
Credit Card Fraud Detection Using Autoencoder Neural Network
https://github.com/akalia77/hinet
HINet: Half Instance Normalization Network for Image Restoration
https://github.com/akashsonowal/ddpo-pytorch
https://github.com/akshaymehra24/poisoning_certified_defenses
https://github.com/alanqrwang/RegAgnosticCSMRI
Computing Multiple Image Reconstructions with a Single Hypernetwork
https://github.com/albarji/toy-diffusion
https://github.com/albrateanu/LYT-Net/tree/main/PyTorch
LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement
https://github.com/aleks-krasowski/pinnfluence_resampling
https://github.com/alexander-prutko/mil-audio-denoising
https://github.com/alexzhou907/pvd
3D Shape Generation and Completion through Point-Voxel Diffusion
https://github.com/ali-vilab/VGen
https://github.com/ali-vilab/i2vgen-xl
https://github.com/alibaba/graph-gpt
https://github.com/alipay/diffusion-model-for-instance-segmentation
https://github.com/allenai/scientific-claim-generation
https://github.com/amazon-science/alexa-teacher-models
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
https://github.com/ambareeshsrja16/Python-Module-for-Missing-Data-Imputation
https://github.com/anas-zafar/SSAMAN
https://github.com/anassbellache/TomoGAN
TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks
https://github.com/anchen1011/toflow
https://github.com/andreamazzitelli/ProjectNN
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/andreasfloros/diffusion
https://github.com/andrerclaudio/channel_estimation
https://github.com/anicolson/DeepXi
https://github.com/anindex/noprop
NoProp: Training Neural Networks without Back-propagation or Forward-propagation
https://github.com/ankanbhunia/Handwriting-Transformers
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/ankitf/artifact_reduction_jpeg
Compression Artifacts Reduction by a Deep Convolutional Network
https://github.com/anktplwl91/Image-Superresolution
https://github.com/anonymous0x233/ReuseAndDiffuse
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
https://github.com/anselmeamekoe/ImageDenoisingNN3D
Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising
https://github.com/ansfl/learning-based-mems-gyrocompassing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.