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 4826–4850 of 7282 papers

TitleStatusHype
Diffusion Autoencoders: Toward a Meaningful and Decodable RepresentationCode1
EdiBERT, a generative model for image editingCode1
RADU: Ray-Aligned Depth Update Convolutions for ToF Data DenoisingCode0
Trust the Critics: Generatorless and Multipurpose WGANs with Initial Convergence GuaranteesCode0
IDR: Self-Supervised Image Denoising via Iterative Data RefinementCode1
Vector Quantized Diffusion Model for Text-to-Image SynthesisCode1
Image denoising by Super Neurons: Why go deep?—0
Unsupervised Image Denoising with Frequency Domain KnowledgeCode1
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCode1
Joint inference and input optimization in equilibrium networksCode0
Exploring Versatile Prior for Human Motion via Motion Frequency GuidanceCode1
Fast mesh denoising with data driven normal filtering using deep variational autoencodersCode1
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising—0
Deep Point Cloud Reconstruction—0
ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningCode2
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)—0
One-shot Weakly-Supervised Segmentation in Medical ImagesCode1
Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising—0
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms—0
Enhanced countering adversarial attacks via input denoising and feature restoringCode0
IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact RemovalCode1
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm—0
Restormer: Efficient Transformer for High-Resolution Image RestorationCode1
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image RestorationCode1
CLMB: deep contrastive learning for robust metagenomic binningCode0
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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