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 5051–5100 of 7282 papers

TitleStatusHype
Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models—0
Unsupervised Reservoir Computing for Multivariate Denoising of Severely Contaminated Signals—0
Score-based Diffusion Models in Function Space—0
Unsupervised Sketch to Photo Synthesis—0
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions—0
Score-based Generative Models with Adaptive Momentum—0
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction—0
Score-Based Model for Low-Rank Tensor Recovery—0
X-GANs: Image Reconstruction Made Easy for Extreme Cases—0
Score-based Self-supervised MRI Denoising—0
Score-Based Turbo Message Passing for Plug-and-Play Compressive Image Recovery—0
Score Distillation Sampling with Learned Manifold Corrective—0
Score Distillation via Reparametrized DDIM—0
XGPT: Cross-modal Generative Pre-Training for Image Captioning—0
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
ScoreHypo: Probabilistic Human Mesh Estimation with Hypothesis Scoring—0
Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems—0
Score matching for sub-Riemannian bridge sampling—0
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation—0
Score-Optimal Diffusion Schedules—0
Unsupervised Visual Defect Detection with Score-Based Generative Model—0
Untrained Perceptual Loss for image denoising of line-like structures in MR images—0
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization—0
Screen Content Image Segmentation Using Sparse-Smooth Decomposition—0
S-DCCRN: Super Wide Band DCCRN with learnable complex feature for speech enhancement—0
SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation—0
SDDPM: Speckle Denoising Diffusion Probabilistic Models—0
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation—0
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution—0
SDeMorph: Towards Better Facial De-morphing from Single Morph—0
SD-GAN: Structural and Denoising GAN reveals facial parts under occlusion—0
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain—0
Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification—0
Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction—0
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement—0
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising—0
SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation—0
Accelerate High-Quality Diffusion Models with Inner Loop Feedback—0
Searching Efficient Model-guided Deep Network for Image Denoising—0
SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer—0
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models—0
Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners—0
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition—0
Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising—0
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction—0
seg2med: a bridge from artificial anatomy to multimodal medical images—0
Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning—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