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 6226–6250 of 7282 papers

TitleStatusHype
VibrantLeaves: A principled parametric image generator for training deep restoration modelsCode0
Few Clean Instances Help Denoising Distant SupervisionCode0
Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRICode0
Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple SclerosisCode0
Few-shot Image Generation with Diffusion ModelsCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image DenoisingCode0
Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion featuresCode0
Iterative PET Image Reconstruction Using Convolutional Neural Network RepresentationCode0
FFDNet: Toward a Fast and Flexible Solution for CNN based Image DenoisingCode0
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT PerfusionCode0
SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision AgricultureCode0
Systematic Evaluation of Neural Retrieval Models on the Touché 2020 Argument Retrieval Subset of BEIRCode0
Speech Dereverberation with a Reverberation Time Shortening TargetCode0
MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural NetworksCode0
Iterative Residual CNNs for Burst Photography ApplicationsCode0
Speech Enhancement based on Denoising Autoencoder with Multi-branched EncodersCode0
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-RatesCode0
VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera NetworkCode0
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising NetworkCode0
Denoising Diffusion Probabilistic Models as a Defense against Adversarial AttacksCode0
Recurrent Self-Supervised Video Denoising with Denser Receptive FieldCode0
Recurrent Spike-based Image Restoration under General IlluminationCode0
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image DenoisingCode0
FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion ModelsCode0
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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