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 4676–4700 of 7282 papers

TitleStatusHype
Learning Continuous Face Representation with Explicit Functions—0
Learning Convex Regularizers for Optimal Bayesian Denoising—0
Learning Deep Convolutional Networks for Demosaicing—0
Learning Deep Image Priors for Blind Image Denoising—0
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection—0
WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration—0
Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation—0
Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method—0
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
Learning _1-based analysis and synthesis sparsity priors using bi-level optimization—0
Learning Enriched Features for Fast Image Restoration and Enhancement—0
Tiny Object Detection with Single Point Supervision—0
Learning Entity Representation for Entity Disambiguation—0
Learning Fair Face Representation With Progressive Cross Transformer—0
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora—0
Learning Filter Bank Sparsifying Transforms—0
Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration—0
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding—0
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram—0
Learning from Noisy Labels for Long-tailed Data via Optimal Transport—0
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation—0
Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling—0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
SPIRE: Semantic Prompt-Driven Image Restoration—0
Learning From Unpaired Data: A Variational Bayes Approach—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