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 3026–3050 of 7282 papers

TitleStatusHype
A Face Fairness Framework for 3D Meshes—0
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting—0
Evaluating the design space of diffusion-based generative models—0
4D X-Ray CT Reconstruction using Multi-Slice Fusion—0
From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration—0
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack—0
From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction—0
Evaluating deep variational autoencoders trained on pan-cancer gene expression—0
DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models—0
Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images—0
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images—0
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images—0
From Simulated to Visual Data: A Robust Low-Rank Tensor Completion Approach using lp-Regression for Outlier Resistance—0
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain—0
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering—0
Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets—0
Epsilon-VAE: Denoising as Visual Decoding—0
Convolutional Dictionary Regularizers for Tomographic Inversion—0
Estimating User Location in Social Media with Stacked Denoising Auto-encoders—0
DeBaRA: Denoising-Based 3D Room Arrangement Generation—0
Fully-Connected CRFs with Non-Parametric Pairwise Potential—0
A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders—0
Graph filtering over expanding graphs—0
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
Convolutional Deep Denoising Autoencoders for Radio Astronomical 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