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 1751–1775 of 7282 papers

TitleStatusHype
D^4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-OnCode1
MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI—0
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models—0
Deep Learning CT Image Restoration using System Blur and Noise Models—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
RGB2Point: 3D Point Cloud Generation from Single RGB Images—0
Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise—0
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
M2D2M: Multi-Motion Generation from Text with Discrete Diffusion Models—0
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial NetworkCode0
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net—0
Underwater Acoustic Signal Denoising Algorithms: A Survey of the State-of-the-art—0
Mask2Map: Vectorized HD Map Construction Using Bird's Eye View Segmentation MasksCode2
EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion ModelsCode0
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection—0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays—0
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMMCode0
GeoGuide: Geometric guidance of diffusion modelsCode0
Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic SystemsCode5
Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracyCode1
IMAGDressing-v1: Customizable Virtual DressingCode5
Chip Placement with Diffusion ModelsCode1
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
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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