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/ypotdevin/randomized-defenses
Towards Deep Neural Network Architectures Robust to Adversarial Examples
https://github.com/yqx7150/EASEL
Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
https://github.com/yqx7150/JGM
Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
https://github.com/yqx7150/ugm-pi
Wavelet Transform-assisted Adaptive Generative Modeling for Colorization
https://github.com/ys-koshelev/lgd
https://github.com/yuantiku/fairseq-gec
https://github.com/yuke93/Path-Restore
Path-Restore: Learning Network Path Selection for Image Restoration
https://github.com/yunpeng-shi/fast-cryoem-pca
Ab-initio Contrast Estimation and Denoising of Cryo-EM Images
https://github.com/yyunon/reproducibility-project-group-71
https://github.com/yyyyyxie/DNTextSpotter
DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising Training
https://github.com/z-bingo/Deformable-Kernels-For-Video-Denoising
https://github.com/z-bingo/Recurrent-Fully-Convolutional-Networks
End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks
https://github.com/z-bingo/kernel-prediction-networks-PyTorch
https://github.com/zcablii/sardet_100k
DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images
https://github.com/zehong-ma/emloc
Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model
https://github.com/zekedran/deep_image_prior
https://github.com/zengyi-li/MDSM
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching
https://github.com/zgctroy/layoutdiffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/zgongkuang/IterativeCNN
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
https://github.com/zhajiahe/Token_Drop
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
https://github.com/zhangximing213/denoise
CycleISP: Real Image Restoration via Improved Data Synthesis
https://github.com/zhaozixiang1228/if-film
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
https://github.com/zhaozixiang1228/mmif-cddfuse
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
https://github.com/zhaozixiang1228/mmif-emma
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
https://github.com/zhdbwe/Paper-DailyReading
https://github.com/zheangh/dfg
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
https://github.com/zhengchen1999/cat
Spatial-Spectral Transformer for Hyperspectral Image Denoising
https://github.com/zhenzhiwang/intercontrol
https://github.com/zhiminzhang0830/D3PM_Paddle
Structured Denoising Diffusion Models in Discrete State-Spaces
https://github.com/zhouxian/FluidLab
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
https://github.com/zhuangpeiyu/reloc
Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis
https://github.com/zhuangshaobin/Video-GPT
https://github.com/zhuangyunliang/dpfnet
https://github.com/zhyack/UDRG
A Semi-Supervised Approach for Low-Resourced Text Generation
https://github.com/zhyzhouu/amed-solver
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.