SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1475 of 7282 papers

TitleStatusHype
Simple GNN Regularisation for 3D Molecular Property Prediction & BeyondCode1
SinIR: Efficient General Image Manipulation with Single Image ReconstructionCode1
Non Gaussian Denoising Diffusion ModelsCode1
Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean ImagesCode1
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
Adversarial purification with Score-based generative modelsCode1
TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT DenoisingCode1
MemStream: Memory-Based Streaming Anomaly DetectionCode1
Uformer: A General U-Shaped Transformer for Image RestorationCode1
Neural Architecture Search via Bregman IterationsCode1
ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language GenerationCode1
Unsharp Mask Guided FilteringCode1
Beyond the Spectrum: Detecting Deepfakes via Re-SynthesisCode1
RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral FeaturesCode1
FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian NoiseCode1
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and BeyondCode1
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: ReportCode1
Window-Level is a Strong Denoising SurrogateCode1
HINet: Half Instance Normalization Network for Image RestorationCode1
Disentangling Noise from Images: A Flow-Based Image Denoising Neural NetworkCode1
A Bregman Learning Framework for Sparse Neural NetworksCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
AdaGNN: Graph Neural Networks with Adaptive Frequency Response FilterCode1
Invertible Denoising Network: A Light Solution for Real Noise RemovalCode1
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified