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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.
Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising
Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model
Unfolded proximal neural networks for robust image Gaussian denoising
SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation
Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review
Diffusion Model for Camouflaged Object Detection
Domain Adaptation based on Human Feedback for Enhancing Generative Model Denoising Abilities
Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Towards General Low-Light Raw Noise Synthesis and Modeling
Random Sub-Samples Generation for Self-Supervised Real Image Denoising
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image Restoration
ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder
Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment Loss
Generative Prompt Model for Weakly Supervised Object Localization
A Novel Truncated Norm Regularization Method for Multi-channel Color Image Denoising
ExposureDiffusion: Learning to Expose for Low-light Image Enhancement
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR
Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing
Image Denoising and the Generative Accumulation of Photons
On the Importance of Denoising when Learning to Compress Images
Image Reconstruction using Enhanced Vision Transformer
Timbre transfer using image-to-image denoising diffusion implicit models
Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer
Training Energy-Based Models with Diffusion Contrastive Divergences
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
Designing Stable Neural Networks using Convex Analysis and ODEs
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
SVNR: Spatially-variant Noise Removal with Denoising Diffusion
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
PromptIR: Prompting for All-in-One Blind Image Restoration
Weighted structure tensor total variation for image denoising
Structural Restricted Boltzmann Machine for image denoising and classification
CANDID: Correspondence AligNment for Deep-burst Image Denoising
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