Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/hurunyi/videoshield
공식 코드VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking
https://github.com/hust-tan/diffusionvi
공식 코드Diffusion Priors for Variational Likelihood Estimation and Image Denoising
https://github.com/hustvl/diffusiondrive
공식 코드DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
https://github.com/hutaihang/faster-diffusion
공식 코드Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference
https://github.com/hyao1/glad
공식 코드https://github.com/hyeonsoojo/metagc
공식 코드https://github.com/hygenie1228/cycleadapt_release
공식 코드Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction
https://github.com/hytidel/unetreweighting
공식 코드Dynamic Importance in Diffusion U-Net for Enhanced Image Synthesis
https://github.com/hzfinfdu/diffusion-bert
공식 코드DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models
https://github.com/hzzone/du-gan
공식 코드https://github.com/ian-jihoonpark/X-Diffusion
공식 코드Explaining generative diffusion models via visual analysis for interpretable decision-making process
https://github.com/ibm/baddiffusion
공식 코드https://github.com/ibm/villandiffusion
공식 코드VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
https://github.com/ibrahimethemhamamci/hierarchicaldet
공식 코드Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
https://github.com/icon-lab/SelfRDB
공식 코드Self-Consistent Recursive Diffusion Bridge for Medical Image Translation
https://github.com/icon-lab/denomamba
공식 코드DenoMamba: A fused state-space model for low-dose CT denoising
https://github.com/ictmcg/make-your-anchor
공식 코드Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
https://github.com/id-fr/idfr
공식 코드Enhanced countering adversarial attacks via input denoising and feature restoring
https://github.com/idephics/diffsamp
공식 코드Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
https://github.com/idoamos/not-from-scratch
공식 코드Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
https://github.com/idsia/fpainter
공식 코드Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules
https://github.com/ifryed/HyperRes
공식 코드https://github.com/igitugraz/weatherdiffusion
공식 코드Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
https://github.com/iip-group/blind_and_nonparametric_estimators
공식 코드Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals
https://github.com/iit-pavis/diffassemble
공식 코드DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly
https://github.com/ilonafan/dar-noisy-labels
공식 코드Exploring Video-Based Driver Activity Recognition under Noisy Labels
https://github.com/imankhazrak/DDPM_X-Ray
공식 코드https://github.com/imlixinyang/director3d
공식 코드Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text
https://github.com/inbarhub/ddpm_inversion
공식 코드An Edit Friendly DDPM Noise Space: Inversion and Manipulations
https://github.com/inceturker/blind-restoration-of-real-world-audio-by-1d-operational-gans
공식 코드Blind Restoration of Real-World Audio by 1D Operational GANs
https://github.com/indigopurple/mag
공식 코드Point Cloud Denoising via Momentum Ascent in Gradient Fields
https://github.com/indigopurple/manet
공식 코드MANet: Improving Video Denoising with a Multi-Alignment Network
https://github.com/indu1ge/DepthMaster
공식 코드DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
https://github.com/infzhou/sam-pd
공식 코드SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising
https://github.com/inhwanbae/singulartrajectory
공식 코드SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.