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 4926–4950 of 7282 papers

TitleStatusHype
Efficient and Flexible Sublabel-Accurate Energy MinimizationCode0
0/1 Deep Neural Networks via Block Coordinate Descent—0
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix—0
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score MatchingCode0
To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets—0
Defending Observation Attacks in Deep Reinforcement Learning via Detection and DenoisingCode0
Robust Time Series Denoising with Learnable Wavelet Packet Transform—0
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal—0
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models—0
Denoising Generalized Expectation-Consistent Approximation for MR Image RecoveryCode0
AGConv: Adaptive Graph Convolution on 3D Point CloudsCode0
Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets—0
Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective—0
Robust Deep Ensemble Method for Real-world Image DenoisingCode0
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models—0
Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models—0
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings—0
Enhanced precision of circadian rhythm by output systemCode0
Modeling sRGB Camera Noise with Normalizing Flows—0
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images—0
CUNI Submission to MT4All Shared Task—0
DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder—0
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning—0
Far-Field Speaker Recognition Benchmark Derived From The DiPCo Corpus—0
Differentiable programming for functional connectomicsCode0
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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