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Image Denoising
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Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.
<span style="color:grey; opacity: 0.6">( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Physics-augmented Deep Learning with Adversarial Domain Adaptation: Applications to STM Image Denoising
A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising
Sigma Flows for Image and Data Labeling and Learning Structured Prediction
Single Image Denoising via a New Lightweight Learning-Based Model
A Preliminary Exploration Towards General Image Restoration
Perception-based multiplicative noise removal using SDEs
A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers
Deep Joint Denoising and Detection for Enhanced Intracellular Particle Analysis
Learned denoising with simulated and experimental low-dose CT data
Image Denoising Using Green Channel Prior
Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models
Learned Single-Pass Multitasking Perceptual Graphics for Immersive Displays
Interpreting Low-level Vision Models with Causal Effect Maps
Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable Surrogates
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant Attention
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMM
Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracy
Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images Denoising
Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV
Region Attention Transformer for Medical Image Restoration
ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration
Asymmetric Mask Scheme for Self-Supervised Real Image Denoising
Heterogeneous window transformer for image denoising
Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation
Zero-Shot Image Denoising for High-Resolution Electron Microscopy
A Dictionary Based Approach for Removing Out-of-Focus Blur
Fast Unsupervised Tensor Restoration via Low-rank Deconvolution
Beyond the Visible: Jointly Attending to Spectral and Spatial Dimensions with HSI-Diffusion for the FINCH Spacecraft
Hybrid Spatial-spectral Neural Network for Hyperspectral Image Denoising
MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
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