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 5201–5225 of 7282 papers

TitleStatusHype
Image Denoising in FPGA using Generic Risk Estimation—0
S-DCCRN: Super Wide Band DCCRN with learnable complex feature for speech enhancement—0
PromptBERT: Improving BERT Sentence Embeddings with Prompts—0
A Light Label Denoising Method with the Internal Data Guidance—0
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog—0
Distill and Calibrate: Denoising Inconsistent Labeling Instances for Chinese Named Entity Recognition—0
Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data—0
Deep-Learning Inversion Method for the Interpretation of Noisy Logging-While-Drilling Resistivity Measurements—0
Fingerprint Presentation Attack Detection by Channel-wise Feature DenoisingCode0
Moment Transform-Based Compressive Sensing in Image Processing—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
Impact of Benign Modifications on Discriminative Performance of Deepfake Detectors—0
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes—0
Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization—0
FINO: Flow-based Joint Image and Noise Model—0
Joint Neural AEC and Beamforming with Double-Talk Detection—0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Estimating High Order Gradients of the Data Distribution by Denoising—0
Statistical and Computational Efficiency for Smooth Tensor Estimation with Unknown Permutations—0
Survey of Deep Learning Methods for Inverse Problems—0
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet—0
Graph Denoising with Framelet RegularizerCode0
Testing using Privileged Information by Adapting Features with Statistical Dependence—0
Deep Point Set Resampling via Gradient Fields—0
Zero-Shot Translation using Diffusion Models—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