Papers with Code 아카이브

Image Denoising

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 2
평가표: 21
보존된 평가 기록: 110

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>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/jingweisong/uncertainty-estimation-for-hyper-spectral-image-denoising

공식 코드

Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation

https://github.com/12138-chr/mtair

공식 코드

Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation

https://github.com/491506870/PRIDNet

공식 코드

Pyramid Real Image Denoising Network

https://github.com/90n9-yu/PT-WGAN

공식 코드

Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising

https://github.com/AliRafiee7/SeConvNet

공식 코드

A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

https://github.com/AmmarMian/huber_mm_framework

공식 코드

Block-wise Minimization-Majorization algorithm for Huber's criterion: sparse learning and applications

https://github.com/Arnontu/DeepAudioWaveformPrior

공식 코드

Deep Audio Waveform Prior

https://github.com/Ascend-Research/CascadedGaze

공식 코드

CascadedGaze: Efficiency in Global Context Extraction for Image Restoration

https://github.com/Bartzi/one-model-to-reconstruct-them-all

공식 코드

One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN

https://github.com/BorealisAI/noise_flow

공식 코드

Noise Flow: Noise Modeling with Conditional Normalizing Flows

https://github.com/DIAL-RPI/Task-Oriented-CT-Denoising_TOD-Net

공식 코드

Task-Oriented Low-Dose CT Image Denoising

https://github.com/Dandy5721/GNN-PDE-COV

공식 코드

Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals

https://github.com/DawyD/cnn-map-denoiser

공식 코드

Image Restoration using Plug-and-Play CNN MAP Denoisers

https://github.com/Ding-Liu/DeepDenoising

공식 코드

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

https://github.com/Ding-Liu/NLRN

공식 코드

Non-Local Recurrent Network for Image Restoration

https://github.com/Fabio-Gil-Z/IRUNet

공식 코드

Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network

https://github.com/FanChiMao/SRMNet

공식 코드

Selective Residual M-Net for Real Image Denoising

https://github.com/FengleiFan/QAE

공식 코드

Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising

https://github.com/GuoShi28/CBDNet

공식 코드

Toward Convolutional Blind Denoising of Real Photographs

https://github.com/HWQuantum/HistNet

공식 코드

Robust super-resolution depth imaging via a multi-feature fusion deep network

https://github.com/HilaManor/GaussianDenoisingPosterior

공식 코드

On the Posterior Distribution in Denoising: Application to Uncertainty Quantification

https://github.com/Holmes-Alan/dSRVAE

공식 코드

Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder

https://github.com/IVRL/BUIFD

공식 코드

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

https://github.com/IljaManakov/HDcycleGAN

공식 코드

Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation

https://github.com/JK-the-Ko/Self2SelfPlus

공식 코드

Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment Loss

https://github.com/JWSoh/DUBD

공식 코드

Deep Universal Blind Image Denoising

https://github.com/JWSoh/VDIR

공식 코드

Variational Deep Image Denoising

https://github.com/JunjieHuangICL/LINN

공식 코드

LINN: Lifting Inspired Invertible Neural Network for Image Denoising

https://github.com/KindXiaoming/QHMC

공식 코드

Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling

https://github.com/LabForComputationalVision/bias_free_denoising

공식 코드

Robust and interpretable blind image denoising via bias-free convolutional neural networks

https://github.com/LinaZhuang/FastHyDe_FastHyIn

공식 코드

Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations

https://github.com/LinaZhuang/RobustHyDenoiser-RhyDe-

공식 코드

Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations

https://github.com/Lotayou/Face-Renovation

공식 코드

HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment

https://github.com/MC-E/COLA-Net

공식 코드

COLA-Net: Collaborative Attention Network for Image Restoration

https://github.com/MHmi1/Image_Denoising_Classification

공식 코드

Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification Tasks

https://github.com/MLI-lab/ConvDecoder

공식 코드

Accelerated MRI with Un-trained Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.