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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.
Time Will Tell: Harness Time to Denoise Collaborative Filtering
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning
Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation
Generalizable dose denoising using diffusion transformer framework for pencil beam scanning proton therapy
fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting
Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models
Similarity-Guided Diffusion for Contrastive Sequential Recommendation
HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing
AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air
A statistical physics framework for optimal learning
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models
Unconditional Diffusion for Generative Sequential Recommendation
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model
Energy-Based Transformers are Scalable Learners and Thinkers
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Score-Based Model for Low-Rank Tensor Recovery
From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging
Learning to See in the Extremely Dark
Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou
SmoothSinger: A Conditional Diffusion Model for Singing Voice Synthesis with Multi-Resolution Architecture
Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising
Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
Ctrl-Z Sampling: Diffusion Sampling with Controlled Random Zigzag Explorations
TDiR: Transformer based Diffusion for Image Restoration Tasks
DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation
Continuous-variable Quantum Diffusion Model for State Generation and Restoration
NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs
Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.