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 3776–3800 of 7282 papers

TitleStatusHype
A Data-Driven Paradigm-Based Image Denoising and Mosaicking Approach for High-Resolution Acoustic Camera—0
A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process—0
MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation—0
Making Images from Images: Interleaving Denoising and Transformation—0
MalPhase: Fine-Grained Malware Detection Using Network Flow Data—0
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising—0
Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN—0
Towards Consistent and Controllable Image Synthesis for Face Editing—0
ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation—0
ManiDext: Hand-Object Manipulation Synthesis via Continuous Correspondence Embeddings and Residual-Guided Diffusion—0
Towards Controllable Diffusion Models via Reward-Guided Exploration—0
Manifold Learning Benefits GANs—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"—0
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches—0
Manifold Rewiring for Unlabeled Imaging—0
Map2Traj: Street Map Piloted Zero-shot Trajectory Generation with Diffusion Model—0
MAP-based Problem-Agnostic diffusion model for Inverse Problems—0
MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D Generation—0
MARBLE: Material Recomposition and Blending in CLIP-Space—0
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems—0
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion—0
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks—0
Masked and Shuffled Blind Spot Denoising for Real-World Images—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