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 6826–6850 of 7282 papers

TitleStatusHype
Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration—0
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The _0 Method—0
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning—0
Efficient Training for Visual Tracking with Deformable Transformer—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination—0
Efficient Unsupervised NMT for Related Languages with Cross-Lingual Language Models and Fidelity Objectives—0
Efficient Video and Audio processing with Loihi 2—0
Supervised Dictionary Learning and Sparse Representation-A Review—0
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems—0
Eformer: Edge Enhancement based Transformer for Medical Image Denoising—0
Supervised learning of analysis-sparsity priors with automatic differentiation—0
EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation—0
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems—0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
Electroencephalogram Signal Processing with Independent Component Analysis and Cognitive Stress Classification using Convolutional Neural Networks—0
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network—0
_0 Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees—0
_1-K-SVD: A Robust Dictionary Learning Algorithm With Simultaneous Update—0
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis—0
Supervised Learning of Sparsity-Promoting Regularizers for Denoising—0
Emage: Non-Autoregressive Text-to-Image Generation—0
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising—0
Embedding models through the lens of Stable Coloring—0
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding—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