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

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Showing 1176–1200 of 7282 papers

TitleStatusHype
Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks—0
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models—0
Novel View Extrapolation with Video Diffusion Priors—0
DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh—0
DT-LSD: Deformable Transformer-based Line Segment DetectionCode1
EEG Signal Denoising Using pix2pix GAN: Enhancing Neurological Data Analysis—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic SegmentationCode1
Adversarial Diffusion Compression for Real-World Image Super-ResolutionCode4
Posterior Sampling for Random Noise Attenuation via Score-based Generative ModelsCode0
A Neural Denoising Vocoder for Clean Waveform Generation from Noisy Mel-Spectrogram based on Amplitude and Phase Predictions—0
mDAE : modified Denoising AutoEncoder for missing data imputation—0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
Robust multi-coil MRI reconstruction via self-supervised denoisingCode0
Self-supervised denoising of visual field data improves detection of glaucoma progression—0
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models—0
Dataset Distillers Are Good Label Denoisers In the WildCode0
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
MSSIDD: A Benchmark for Multi-Sensor DenoisingCode0
Aligning Few-Step Diffusion Models with Dense Reward Difference LearningCode1
Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category PreferencesCode0
KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems—0
Time Step Generating: A Universal Synthesized Deepfake Image DetectorCode0
Test-time Conditional Text-to-Image Synthesis Using Diffusion Models—0
Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume—0
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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