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 2526–2550 of 7282 papers

TitleStatusHype
Mixed geometry information regularization for image multiplicative denoising—0
GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space—0
Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising—0
Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising—0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation—0
Bright-NeRF:Brightening Neural Radiance Field with Color Restoration from Low-light Raw Images—0
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations—0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models—0
Consistent Human Image and Video Generation with Spatially Conditioned DiffusionCode0
Enhancing Diffusion Models for High-Quality Image Generation—0
Guided Diffusion Model for Sensor Data Obfuscation—0
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation—0
DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On—0
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning—0
Federated Unlearning Model Recovery in Data with Skewed Label Distributions—0
MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing—0
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion—0
Personalized Generative Low-light Image Denoising and Enhancement—0
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling—0
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image EnhancementCode0
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory PredictionCode0
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image GenerationCode0
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K SamplingCode0
LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers—0
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration—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