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 3301–3325 of 7282 papers

TitleStatusHype
Autoregressive Diffusion Model for Graph GenerationCode1
Not All Steps are Created Equal: Selective Diffusion Distillation for Image ManipulationCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Noise-aware Speech Enhancement using Diffusion Probabilistic ModelCode1
A Novel Truncated Norm Regularization Method for Multi-channel Color Image DenoisingCode0
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR—0
ExposureDiffusion: Learning to Expose for Low-light Image EnhancementCode1
Certified Robustness for Large Language Models with Self-DenoisingCode1
Image Denoising and the Generative Accumulation of PhotonsCode1
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—0
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0—0
Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing—0
Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)—0
On the Importance of Denoising when Learning to Compress ImagesCode0
Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models—0
Exposing the Fake: Effective Diffusion-Generated Images Detection—0
Geometric Neural Diffusion ProcessesCode1
Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising—0
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models—0
DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization—0
Image Reconstruction using Enhanced Vision Transformer—0
Metropolis Sampling for Constrained Diffusion Models—0
Timbre transfer using image-to-image denoising diffusion implicit models—0
Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes—0
Learning Spatial Features from Audio-Visual Correspondence in Egocentric Videos—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