https://github.com/VCG-team/DiffSegmenter
공식 코드Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter
Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 6
- 보존된 평가 기록
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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/VCL3D/DeepDepthDenoising
공식 코드https://github.com/VITA-Group/CERL
공식 코드CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noise
https://github.com/VMBoehm/PAE
공식 코드https://github.com/Vandermode/NoiseModel
공식 코드A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising
https://github.com/Vandermode/QRNN3D
공식 코드3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
https://github.com/Vandermode/TFPnP
공식 코드TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
https://github.com/VishnuJay/Determination-of-Trace-Organic-Contaminant-Concentration-via-Machine-Classification-of-Raman-Spectra
공식 코드https://github.com/WHUQZhang/HSID-CNN
공식 코드https://github.com/WHUQZhang/SSGN
공식 코드Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network
https://github.com/Waino/OpenNMT-py
공식 코드Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation
https://github.com/WaveOpt-Lab/YES-bound
공식 코드Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning
https://github.com/WeiWangTrento/Power-Iteration-SVD
공식 코드https://github.com/WeishengDong/DPDNN_PyTorch
공식 코드Denoising Prior Driven Deep Neural Network for Image Restoration
https://github.com/Weixin-Liang/HERALD
공식 코드HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations
https://github.com/WenCongWu/DCBDNet
공식 코드Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection
https://github.com/WenCongWu/DRANet
공식 코드https://github.com/WenjieWWJ/DenoisingRec
공식 코드https://github.com/Wenlin-Chen/DiGS
공식 코드https://github.com/Wi-sc/ViewFusion
공식 코드ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
https://github.com/XHWXD/DBSN
공식 코드https://github.com/XLearning-SCU/2025-CVPR-MaIR
공식 코드MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
https://github.com/XML124/CDRN-channel-estimation-IRS
공식 코드https://github.com/Y-debug-sys/DDPM-TME
공식 코드Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model
https://github.com/Y-debug-sys/DTM
공식 코드https://github.com/YCL92/LowlightENR
공식 코드Progressive Joint Low-light Enhancement and Noise Removal for Raw Images
https://github.com/YanbinHe/JournalDecomOffGrid
공식 코드Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured Signals
https://github.com/Yang-Liu1082/FDN
공식 코드Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
https://github.com/Yang-Liu1082/InvDN
공식 코드Invertible Denoising Network: A Light Solution for Real Noise Removal
https://github.com/Yasoz/DiffTraj
공식 코드DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
https://github.com/Ydo-W/DparNet
공식 코드Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning
https://github.com/YilinLiu97/Faster-DIP-Recon
공식 코드https://github.com/YooJiHyeong/SinIR
공식 코드SinIR: Efficient General Image Manipulation with Single Image Reconstruction
https://github.com/YoujiaZhang/USD
공식 코드Optimized View and Geometry Distillation from Multi-view Diffuser
https://github.com/YoungJooHan/NM-FlowGAN
공식 코드https://github.com/YoushanZhang/CoxImgSwinTransformer
공식 코드Complex Image Generation SwinTransformer Network for Audio Denoising
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.