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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.
CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction
Addressing Missing Data Issue for Diffusion-based Recommendation
Few-Shot Concept Unlearning with Low Rank Adaptation
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion Model
Video-GPT via Next Clip Diffusion
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling
dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching
Multi-Stage Speaker Diarization for Noisy Classrooms
Prior-Guided Diffusion Planning for Offline Reinforcement Learning
DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning
Evolutionary training-free guidance in diffusion model for 3D multi-objective molecular generation
Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review
AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity
A Fourier Space Perspective on Diffusion Models
Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion
CRISP: Clustering Multi-Vector Representations for Denoising and Pruning
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations
Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT Acceleration
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction
Rethinking Circuit Completeness in Language Models: AND, OR, and ADDER Gates
Whitened Score Diffusion: A Structured Prior for Imaging Inverse Problems
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps
Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
TransDiffuser: End-to-end Trajectory Generation with Decorrelated Multi-modal Representation for Autonomous Driving
Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch
Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems
Robust Knowledge Graph Embedding via Denoising
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models
Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations
EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation
ConDiSim: Conditional Diffusion Models for Simulation Based Inference
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.