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 5026–5050 of 7282 papers

TitleStatusHype
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations—0
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors—0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
Low-Dose CT Denoising via Sinogram Inner-Structure Transformer—0
Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments—0
Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization—0
The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models—0
Spectral Denoising for Microphone Classification—0
Zero-shot Blind Image Denoising via Implicit Neural Representations—0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems—0
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping—0
Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey—0
Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising—0
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentationCode0
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning—0
Time-inhomogeneous diffusion geometry and topology—0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations—0
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages—0
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation—0
On the Kullback-Leibler divergence between pairwise isotropic Gaussian-Markov random fields—0
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion—0
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation—0
CRISPnet: Color Rendition ISP Net—0
Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory—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