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 2526–2550 of 7282 papers

TitleStatusHype
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation—0
DDPM based X-ray Image Synthesizer—0
Learning to adapt unknown noise for hyperspectral image denoising—0
Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models—0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection—0
DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding—0
A Hybrid Precipitation Prediction Method based on Multicellular Gene Expression Programming—0
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!—0
AutoEncoders for Training Compact Deep Learning RF Classifiers for Wireless Protocols—0
DDIM-Driven Coverless Steganography Scheme with Real Key—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
A hybrid neuro--wavelet predictor for QoS control and stability—0
DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization—0
Adapting Sentence Transformers for the Aviation Domain—0
Extreme Low-Light Environment-Driven Image Denoising Over Permanently Shadowed Lunar Regions With a Physical Noise Model—0
Factorized Diffusion: Perceptual Illusions by Noise Decomposition—0
A hybrid approach to seismic deblending: when physics meets self-supervision—0
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting—0
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models—0
Adapting MIMO video restoration networks to low latency constraints—0
Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms—0
A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising—0
Dataset Creation Pipeline for Camera-Based Heart Rate Estimation—0
Data Dropout: Optimizing Training Data for Convolutional Neural Networks—0
Attention-based Neural Cellular Automata—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