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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.
Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
Decoupled Data Consistency with Diffusion Purification for Image Restoration
Fast, nonlocal and neural: a lightweight high quality solution to image denoising
Purified and Unified Steganographic Network
On normalization-equivariance properties of supervised and unsupervised denoising methods: a survey
A unified framework of non-local parametric methods for image denoising
RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference -- Application to pulsar observations
Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability
PFCM: Poisson flow consistency models for low-dose CT image denoising
Color Image Denoising Using The Green Channel Prior
InstructIR: High-Quality Image Restoration Following Human Instructions
Masked Pre-training Enables Universal Zero-shot Denoiser
CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping
Two-stage Progressive Residual Dense Attention Network for Image Denoising
Boosting of Implicit Neural Representation-based Image Denoiser
Robust single-particle cryo-EM image denoising and restoration
Revisiting Nonlocal Self-Similarity from Continuous Representation
Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer
Natural Adversarial Patch Generation Method Based on Latent Diffusion Model
Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation
PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models
NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN
Tell Me What You See: Text-Guided Real-World Image Denoising
ReCoRe: Regularized Contrastive Representation Learning of World Model
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models
Prompt-In-Prompt Learning for Universal Image Restoration
Self-supervised OCT Image Denoising with Slice-to-Slice Registration and Reconstruction
TDiffDe: A Truncated Diffusion Model for Remote Sensing Hyperspectral Image Denoising
SDDPM: Speckle Denoising Diffusion Probabilistic Models
Back to Basics: Fast Denoising Iterative Algorithm
L-WaveBlock: A Novel Feature Extractor Leveraging Wavelets for Generative Adversarial Networks
LuminanceL1Loss: A loss function which measures percieved brightness and colour differences
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.