Papers with Code 아카이브
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.
DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images
A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
Investigating Self-Supervised Image Denoising with Denaturation
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data
Noise propagation and MP-PCA image denoising for high-resolution quantitative T2* and magnetic susceptibility mapping (QSM)
LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising
Denoising: from classical methods to deep CNNs
SNRGAN: The Semi Noise Reduction GAN for Image Denoising
A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control
A novel perspective on denoising using quantum localization with application to medical imaging
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification Tasks
Masked and Shuffled Blind Spot Denoising for Real-World Images
WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion
NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset
Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising
Single Stage Adaptive Multi-Attention Network for Image Restoration
LIPT: Latency-aware Image Processing Transformer
Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation
Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping
A CT Image Denoising Method with Residual Encoder-Decoder Network
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
GenesisTex: Adapting Image Denoising Diffusion to Texture Space
Transfer CLIP for Generalizable Image Denoising
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
IIDM: Image-to-Image Diffusion Model for Semantic Image Synthesis
Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising
Eigen-CNN: Eigenimages Plus Eigennoise Level Maps Guided Network for Hyperspectral Image Denoising
WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising
A Spectrum-based Image Denoising Method with Edge Feature Enhancement
Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.