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 4176–4200 of 7282 papers

TitleStatusHype
How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?Code0
Understanding and Tackling Scattering and Reflective Flare for Mobile Camera Systems—0
Pre-Training with Diffusion models for Dental Radiography segmentation—0
Attenuation of Seismic Random Noise With Unknown Distribution: A Gaussianization FrameworkCode0
Gradient-based adaptive wavelet de-noising method for photoacoustic imaging in vivo—0
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis—0
Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity InteractionsCode0
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image RestorationCode0
ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder—0
InFusion: Inject and Attention Fusion for Multi Concept Zero-Shot Text-based Video Editing—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
Dehazing Ultrasound using Diffusion Models—0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
Convergent regularization in inverse problems and linear plug-and-play denoisers—0
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public CloudCode0
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond—0
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
Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing—0
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
On the Importance of Denoising when Learning to Compress ImagesCode0
Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)—0
Exposing the Fake: Effective Diffusion-Generated Images Detection—0
Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models—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