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 6776–6800 of 7282 papers

TitleStatusHype
Edge-based Denoising Image Compression—0
EdgeDiff: Edge-aware Diffusion Network for Building Reconstruction from Point Clouds—0
Edge-enhancing Filters with Negative Weights—0
EDGE++: Improved Training and Sampling of EDGE—0
Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing—0
SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution—0
EDGE-Rec: Efficient and Data-Guided Edge Diffusion For Recommender Systems Graphs—0
SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates—0
A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model—0
SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization—0
EDmamba: A Simple yet Effective Event Denoising Method with State Space Model—0
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF—0
EDNet: A Distortion-Agnostic Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training—0
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis—0
EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models—0
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia—0
Superpixel-Based Building Damage Detection from Post-earthquake Imagery Using Deep Neural Networks—0
EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer—0
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses—0
EEG Signal Denoising Using pix2pix GAN: Enhancing Neurological Data Analysis—0
Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation—0
A Geometric Approach to Color Image Regularization—0
Effective Real Image Editing with Accelerated Iterative Diffusion Inversion—0
Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion—0
Superresolution of Noisy Remotely Sensed Images Through Directional Representations—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