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 5201–5250 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
Action-Conditioned 3D Human Motion Synthesis with Transformer VAECode1
Saddlepoints in Unsupervised Least Squares—0
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation—0
Speech Denoising Without Clean Training Data: A Noise2Noise ApproachCode1
3D Shape Generation and Completion through Point-Voxel DiffusionCode1
On tuning consistent annealed sampling for denoising score matching—0
PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal ProcessingCode1
Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging—0
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps—0
Learning robust speech representation with an articulatory-regularized variational autoencoder—0
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network—0
Searching Efficient Model-guided Deep Network for Image Denoising—0
Noise Estimation for Generative Diffusion Models—0
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs—0
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification—0
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression—0
Interpretable Unsupervised Diversity Denoising and Artefact RemovalCode1
Diff-TTS: A Denoising Diffusion Model for Text-to-Speech—0
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network—0
Variational Deep Image DenoisingCode1
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning—0
Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising—0
Efficient Unsupervised NMT for Related Languages with Cross-Lingual Language Models and Fidelity Objectives—0
Advances and Challenges in Unsupervised Neural Machine Translation—0
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal ProcessingCode0
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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