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 5351–5400 of 7282 papers

TitleStatusHype
No-reference Image Denoising Quality Assessment—0
Mobile Recognition of Wikipedia Featured Sites using Deep Learning and Crowd-sourced Imagery—0
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration—0
Weighted Mean and Median graph Filters with Attenuation Factor for Sensor Network—0
Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models—0
Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection—0
NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model—0
Novel View Extrapolation with Video Diffusion Priors—0
Novel View Synthesis with Diffusion Models—0
N-SfC: Robust and Fast Shape Estimation from Caustic Images—0
NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification—0
Enhancing and Learning Denoiser without Clean Reference—0
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge—0
Nuclear Norm Regularization for Deep Learning—0
Numerical Overcurrent Relay: A Digitizing Element Testing Automation and Simulation Based on Wavelet Transform—0
Revealing Stable and Unstable Modes of Generic Denoisers through Nonlinear Eigenvalue Analysis—0
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification—0
Weighted Nuclear Norm Minimization with Application to Image Denoising—0
Object-centric 3D Motion Field for Robot Learning from Human Videos—0
Object Detection based on OcSaFPN in Aerial Images with Noise—0
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model—0
Object Motion Guided Human Motion Synthesis—0
Weighted-Sampling Audio Adversarial Example Attack—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving—0
OccludeNeRF: Geometric-aware 3D Scene Inpainting with Collaborative Score Distillation in NeRF—0
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement—0
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations—0
ODPG: Outfitting Diffusion with Pose Guided Condition—0
O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions—0
Offloading Deep Learning Powered Vision Tasks from UAV to 5G Edge Server with Denoising—0
Adaptive Denoising of Signals with Local Shift-Invariant Structure—0
Adaptive Cyclic Diffusion for Inference Scaling—0
Adaptive Cohen's Class Time-Frequency Distribution—0
OmniCreator: Self-Supervised Unified Generation with Universal Editing—0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
On 1-Laplacian Elliptic Equations Modeling Magnetic Resonance Image Rician Denoising—0
OnACID: Online Analysis of Calcium Imaging Data in Real Time—0
On a Mechanism Framework of Autoencoders—0
On a new formulation of nonlocal image filters involving the relative rearrangement—0
On a non-local spectrogram for denoising one-dimensional signals—0
On approximating f with neural networks—0
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation—0
TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain—0
On change point detection using the fused lasso method—0
On Convergent Finite Difference Schemes for Variational - PDE Based Image Processing—0
Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction—0
On denoising autoencoders trained to minimise binary cross-entropy—0
On denoising modulo 1 samples of a function—0
On Denoising Walking Videos for Gait Recognition—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