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
AdaGNN: Graph Neural Networks with Adaptive Frequency Response FilterCode1
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images—0
Frequency Domain Loss Function for Deep Exposure Correction of Dark Images—0
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations—0
Invertible Denoising Network: A Light Solution for Real Noise RemovalCode1
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopyCode0
Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image SensorCode1
Removing Adversarial Noise in Class Activation Feature Space—0
A Two-stage Deep Network for High Dynamic Range Image ReconstructionCode0
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging—0
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text GenerationCode1
Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection—0
Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising—0
Neural Transfer Learning for Repairing Security Vulnerabilities in C CodeCode1
Generating Bug-Fixes Using Pretrained Transformers—0
Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder—0
Advanced Deep Networks for 3D Mitochondria Instance SegmentationCode1
Image Super-Resolution via Iterative RefinementCode1
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep LearningCode0
On the Design of Deep Priors for Unsupervised Audio RestorationCode1
Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life PredictionCode0
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding LearningCode1
Holistic Guidance for Occluded Person Re-IdentificationCode0
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding—0
UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic 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