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 3926–3950 of 7282 papers

TitleStatusHype
Functional Neural Networks for Parametric Image Restoration Problems—0
Functions with average smoothness: structure, algorithms, and learning—0
Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications—0
Fundamental limits of Non-Linear Low-Rank Matrix Estimation—0
Fusing Sparsity with Deep Learning for Rotating Scatter Mask Gamma Imaging—0
Target Speech Extraction with Conditional Diffusion Model—0
FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model—0
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation—0
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis—0
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting—0
Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising—0
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration—0
GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising—0
Diffusion Model Compression for Image-to-Image Translation—0
GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space—0
GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers—0
Gated Fusion Network for SAO Filter and Inter Frame Prediction in Versatile Video Coding—0
Gated Recurrent Unit for Video Denoising—0
GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise—0
Gaussian Interpolation Flows—0
多樣訊雜比之訓練語料於降噪自動編碼器其語音強化功能之初步研究 (A Preliminary Study of Various SNR-level Training Data in the Denoising Auto-encoder (DAE) Technique for Speech Enhancement) [In Chinese]—0
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models—0
Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness—0
Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method—0
GazeMoDiff: Gaze-guided Diffusion Model for Stochastic Human Motion Prediction—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