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 7251–7282 of 7282 papers

TitleStatusHype
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced ErrorCode0
Fast and Robust Phase Retrieval via Deep Expectation-Consistent ApproximationCode0
Realistic Noise Synthesis with Diffusion ModelsCode0
RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-ResolutionCode0
ADFormer: Aggregation Differential Transformer for Passenger Demand ForecastingCode0
Synthesising Rare Cataract Surgery Samples with Guided Diffusion ModelsCode0
A Projectional Ansatz to ReconstructionCode0
Certification of Deep Learning Models for Medical Image SegmentationCode0
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence LabelingCode0
Convolutional dictionary learning based auto-encoders for natural exponential-family distributionsCode0
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow EstimationCode0
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace NormCode0
Modeling the Neonatal Brain Development Using Implicit Neural RepresentationsCode0
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy DataCode0
CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation ExtractionCode0
Decouple Learning for Parameterized Image OperatorsCode0
Unsupervised Sentence Compression using Denoising Auto-EncodersCode0
Data-driven modeling of time-domain induced polarizationCode0
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI ReconstructionCode0
Fast Image Restoration With Multi-Bin Trainable Linear UnitsCode0
ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based UpsamplingCode0
Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse ProblemsCode0
Fast LiDAR Upsampling using Conditional Diffusion ModelsCode0
Modular proximal optimization for multidimensional total-variation regularizationCode0
Interspeech 2021 Deep Noise Suppression ChallengeCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Real-world Noisy Image Denoising: A New BenchmarkCode0
Unrolled Optimization with Deep PriorsCode0
Fast Multi-grid Methods for Minimizing Curvature EnergyCode0
There and Back Again: On the relation between noises, images, and their inversions in diffusion modelsCode0
Invariant Risk Minimization Is A Total Variation ModelCode0
Benchmarking multi-component signal processing methods in the time-frequency planeCode0
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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