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 1276–1300 of 7282 papers

TitleStatusHype
Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models—0
There and Back Again: On the relation between noises, images, and their inversions in diffusion modelsCode0
Deep Learning for 3D Point Cloud Enhancement: A Survey—0
NMformer: A Transformer for Noisy Modulation Classification in Wireless CommunicationCode0
bit2bit: 1-bit quanta video reconstruction via self-supervised photon predictionCode1
Consistency Diffusion Bridge ModelsCode2
Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising—0
st-DTPM: Spatial-Temporal Guided Diffusion Transformer Probabilistic Model for Delayed Scan PET Image Prediction—0
Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic ImagingCode0
Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting—0
Temporal and Spatial Super Resolution with Latent Diffusion Model in Medical MRI images—0
RDSinger: Reference-based Diffusion Network for Singing Voice Synthesis—0
RDSA: A Robust Deep Graph Clustering Framework via Dual Soft AssignmentCode0
Refined Risk Bounds for Unbounded Losses via Transductive Priors—0
Class-Aware Contrastive Optimization for Imbalanced Text Classification—0
Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse AutoencodersCode2
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation—0
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks—0
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network—0
A Novel Score-CAM based Denoiser for Spectrographic Signature Extraction without Ground Truth—0
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!—0
On Inductive Biases That Enable Generalization of Diffusion TransformersCode0
Murine AI excels at cats and cheese: Structural differences between human and mouse neurons and their implementation in generative AIsCode0
Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of ConceptCode0
One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation—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