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Video Denoising
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
CIM-NET: A Video Denoising Deep Neural Network Model Optimized for Computing-in-Memory Architectures
UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance
Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable
DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image Animation
Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion
LLVD: LSTM-based Explicit Motion Modeling in Latent Space for Blind Video Denoising
Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion
Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video Denoising
Imagine360: Immersive 360 Video Generation from Perspective Anchor
VidMan: Exploiting Implicit Dynamics from Video Diffusion Model for Effective Robot Manipulation
Video Denoising in Fluorescence Guided Surgery
Progressive Autoregressive Video Diffusion Models
Learning Truncated Causal History Model for Video Restoration
Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier
Temporal As a Plugin: Unsupervised Video Denoising with Pre-Trained Image Denoisers
A Practical Gated Recurrent Transformer Network Incorporating Multiple Fusions for Video Denoising
Adapting MIMO video restoration networks to low latency constraints
Image Denoising Using Green Channel Prior
Lightweight Video Denoising Using a Classic Bayesian Backbone
Cascading Refinement Video Denoising with Uncertainty Adaptivity
IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth Generation
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models
Unsupervised Microscopy Video Denoising
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution
Color Image Denoising Using The Green Channel Prior
VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising
Toward Accurate and Temporally Consistent Video Restoration from Raw Data
Learning in Deep Factor Graphs with Gaussian Belief Propagation
Space-Time Attention with Shifted Non-Local Search
Recurrent Self-Supervised Video Denoising with Denser Receptive Field
Unsupervised Coordinate-Based Video Denoising
Contrastive Learning for Low-light Raw Denoising
RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration
Real-time Controllable Denoising for Image and Video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.