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 2101–2125 of 7282 papers

TitleStatusHype
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering—0
Denoising Click-evoked Otoacoustic Emission Signals by Optimal Shrinkage—0
The Missing U for Efficient Diffusion Models—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
A multi-artifact EEG denoising by frequency-based deep learning—0
Denoising bivariate signals via smoothing and polarization priors—0
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network—0
Denoising-based UNMT is more robust to word-order divergence than MASS-based UNMT—0
Denoising-based Turbo Message Passing for Compressed Video Background Subtraction—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
DriveScape: High-Resolution Driving Video Generation by Multi-View Feature Fusion—0
Denoising based Sequence-to-Sequence Pre-training for Text Generation—0
Denoising based on wavelets and deblurring via self-organizing map for Synthetic Aperture Radar images—0
Denoising-based image reconstruction from pixels located at non-integer positions—0
DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal—0
Denoising Autoregressive Representation Learning—0
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation—0
Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation—0
Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos—0
Adaptive noise imitation for image denoising—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
Beyond Photo Realism for Domain Adaptation from Synthetic Data—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Amortised MAP Inference for Image Super-resolution—0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—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