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 451–475 of 7282 papers

TitleStatusHype
Enhancing LLM Robustness to Perturbed Instructions: An Empirical StudyCode0
Random Conditioning with Distillation for Data-Efficient Diffusion Model Compression—0
Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation—0
Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-TrainingCode0
BioAtt: Anatomical Prior Driven Low-Dose CT Denoising—0
A Unified Approach to Analysis and Design of Denoising Markov Models—0
Bridge the Gap between SNN and ANN for Image Restoration—0
VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in One Step—0
MixerMDM: Learnable Composition of Human Motion Diffusion Models—0
OccludeNeRF: Geometric-aware 3D Scene Inpainting with Collaborative Score Distillation in NeRF—0
Denoising guarantees for optimized sampling schemes in compressed sensing—0
Deep Learning-Based Extended Target Tracking in ISAC Systems—0
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models—0
NoProp: Training Neural Networks without Back-propagation or Forward-propagationCode1
ExScene: Free-View 3D Scene Reconstruction with Gaussian Splatting from a Single Image—0
On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile DevicesCode2
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design SpaceCode2
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model—0
Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model ApproachCode0
Foundation Models For Seismic Data Processing: An Extensive ReviewCode0
SU-YOLO: Spiking Neural Network for Efficient Underwater Object DetectionCode0
RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents—0
Make Autoregressive Great Again: Diffusion-Free Graph Generation with Next-Scale Prediction—0
Enhancing Creative Generation on Stable Diffusion-based ModelsCode1
TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory EncodingCode0
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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