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 3151–3175 of 7282 papers

TitleStatusHype
M2D2M: Multi-Motion Generation from Text with Discrete Diffusion Models—0
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial NetworkCode0
Underwater Acoustic Signal Denoising Algorithms: A Survey of the State-of-the-art—0
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection—0
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMMCode0
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net—0
EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion ModelsCode0
DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays—0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
GeoGuide: Geometric guidance of diffusion modelsCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis—0
Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images DenoisingCode0
Magnetogram-to-Magnetogram: Generative Forecasting of Solar EvolutionCode0
How Control Information Influences Multilingual Text Image Generation and Editing?Code0
Contrastive Sequential-Diffusion Learning: Non-linear and Multi-Scene Instructional Video SynthesisCode0
Physics-Inspired Generative Models in Medical Imaging: A Review—0
Temporal Residual Guided Diffusion Framework for Event-Driven Video Reconstruction—0
Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Images—0
Backdoor Attacks against Image-to-Image Networks—0
Optical Diffusion Models for Image Generation—0
2D Neural Fields with Learned Discontinuities—0
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction—0
Fast and Robust Phase Retrieval via Deep Expectation-Consistent ApproximationCode0
Unsupervised Anomaly Detection Using Diffusion Trend Analysis—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