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 1851–1875 of 7282 papers

TitleStatusHype
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration ModelsCode2
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations—0
Improving Diffusion Inverse Problem Solving with Decoupled Noise AnnealingCode2
ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic ForecastingCode1
Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration—0
Instruct-IPT: All-in-One Image Processing Transformer via Weight ModulationCode3
Posterior Sampling with Denoising Oracles via Tilted Transport—0
Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness—0
Diffusion Models and Representation Learning: A SurveyCode2
Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation—0
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix—0
DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image ClarityCode1
DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative DenoisingCode0
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters—0
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic ForgettingCode0
Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback—0
Denoising as Adaptation: Noise-Space Domain Adaptation for Image RestorationCode2
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks—0
Director3D: Real-world Camera Trajectory and 3D Scene Generation from TextCode3
GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling—0
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy TrainingCode0
FaceScore: Benchmarking and Enhancing Face Quality in Human GenerationCode2
Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation—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