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Denoising
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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>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model
Foundation Models For Seismic Data Processing: An Extensive Review
Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model Approach
NoProp: Training Neural Networks without Back-propagation or Forward-propagation
SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents
Enhancing Creative Generation on Stable Diffusion-based Models
Make Autoregressive Great Again: Diffusion-Free Graph Generation with Next-Scale Prediction
TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory Encoding
Score-Based Turbo Message Passing for Plug-and-Play Compressive Image Recovery
Spatial Transport Optimization by Repositioning Attention Map for Training-Free Text-to-Image Synthesis
Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion
DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal
GCRayDiffusion: Pose-Free Surface Reconstruction via Geometric Consistent Ray Diffusion
RELD: Regularization by Latent Diffusion Models for Image Restoration
Patronus: Bringing Transparency to Diffusion Models with Prototypes
DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image Animation
HORT: Monocular Hand-held Objects Reconstruction with Transformers
Unsupervised Real-World Denoising: Sparsity is All You Need
HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction
Diffusion Image Prior
Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs
Optimal Stepsize for Diffusion Sampling
DeCompress: Denoising via Neural Compression
AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image Restoration
Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model
ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On
Riemannian Optimization on Relaxed Indicator Matrix Manifold
MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D Generation
Exploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling
Diffusion Counterfactuals for Image Regressors
Zero-Shot Audio-Visual Editing via Cross-Modal Delta Denoising
Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.