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 3551–3575 of 7282 papers

TitleStatusHype
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance—0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data—0
Denoising Autoregressive Representation Learning—0
InstructGIE: Towards Generalizable Image Editing—0
Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRICode0
Fréchet Denoised Distance: Enhancing Plausibility Evaluation for Generated Designs with Denoising AutoencoderCode0
The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Random Access in Cellular IoT—0
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt DenoisingCode0
Fundamental limits of Non-Linear Low-Rank Matrix Estimation—0
A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images—0
Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers—0
Fast, nonlocal and neural: a lightweight high quality solution to image denoising—0
Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing—0
Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation—0
Cross-Domain Image Conversion by CycleDM—0
Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection—0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
Improving Adversarial Energy-Based Model via Diffusion Process—0
DiffSal: Joint Audio and Video Learning for Diffusion Saliency Prediction—0
Near-Real-Time Mueller Polarimetric Image Processing for Neurosurgical Intervention—0
List-Mode PET Image Reconstruction Using Dykstra-Like Splitting—0
Point Processes and spatial statistics in time-frequency analysis—0
Graph Generation via Spectral Diffusion—0
Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach—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