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 2351–2375 of 7282 papers

TitleStatusHype
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning—0
Low-dose CT Denoising with Language-engaged Dual-space AlignmentCode1
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data—0
Privacy-Preserving Diffusion Model Using Homomorphic EncryptionCode1
The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Random Access in Cellular IoT—0
Denoising Autoregressive Representation Learning—0
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic AlignmentCode5
Fréchet Denoised Distance: Enhancing Plausibility Evaluation for Generated Designs with Denoising AutoencoderCode0
DiffSF: Diffusion Models for Scene Flow EstimationCode1
Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRICode0
InstructGIE: Towards Generalizable Image Editing—0
Fundamental limits of Non-Linear Low-Rank Matrix Estimation—0
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt DenoisingCode0
Controllable Generation with Text-to-Image Diffusion Models: A SurveyCode5
A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images—0
SSDRec: Self-Augmented Sequence Denoising for Sequential RecommendationCode1
NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and MergingCode2
Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers—0
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-TrainingCode2
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
Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection—0
Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation—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