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 1226–1250 of 7282 papers

TitleStatusHype
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoisingCode1
Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation PriorsCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
3D molecule generation by denoising voxel gridsCode1
Conditional Consistency Guided Image Translation and EnhancementCode1
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image RestorationCode1
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse ProblemsCode1
EGC: Image Generation and Classification via a Diffusion Energy-Based ModelCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion GenerationCode1
Conditional Diffusion Models for Weakly Supervised Medical Image SegmentationCode1
End-to-End Diffusion Latent Optimization Improves Classifier GuidanceCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion ModelsCode1
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text SummarizationCode1
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound NoiseCode1
DFormer: Diffusion-guided Transformer for Universal Image SegmentationCode1
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial DefenseCode1
Conditional score-based diffusion models for Bayesian inference in infinite dimensionsCode1
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large ShiftCode1
3D Shape Generation and Completion through Point-Voxel DiffusionCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
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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