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 5801–5825 of 7282 papers

TitleStatusHype
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration—0
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation—0
Consistent World Models via Foresight Diffusion—0
Constrained Diffusion Implicit Models—0
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections—0
Constrained Posterior Sampling: Time Series Generation with Hard Constraints—0
Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories—0
Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer—0
Space-Time Attention with Shifted Non-Local Search—0
Context-Aware Automated Passenger Counting Data Denoising—0
Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation—0
Context-Aware Prosody Correction for Text-Based Speech Editing—0
Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging—0
Contextual colorization and denoising for low-light ultra high resolution sequences—0
Contextual Text Denoising with Masked Language Models—0
Contextual Text Denoising with Masked Language Model—0
Variational Schrödinger Momentum Diffusion—0
Continuous longitudinal fetus brain atlas construction via implicit neural representation—0
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages—0
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning—0
Continuous Optimization for Fields of Experts Denoising Works—0
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning—0
Continuous-variable Quantum Diffusion Model for State Generation and Restoration—0
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification—0
Contrastive CFG: Improving CFG in Diffusion Models by Contrasting Positive and Negative Concepts—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