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 6301–6325 of 7282 papers

TitleStatusHype
Denoising Monte Carlo Renders with Diffusion ModelsCode0
TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory EncodingCode0
Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise ApproachCode0
Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)Code0
Patch-Ordering as a Regularization for Inverse Problems in Image ProcessingCode0
CUR Transformer: A Convolutional Unbiased Regional Transformer for Image DenoisingCode0
Registration of algebraic varieties using Riemannian optimizationCode0
Joint Multi-Scale Tone Mapping and Denoising for HDR Image EnhancementCode0
When AWGN-based Denoiser Meets Real NoisesCode0
Denoising Noisy Neural Networks: A Bayesian Approach with CompensationCode0
ViDeNN: Deep Blind Video DenoisingCode0
FOCNet: A Fractional Optimal Control Network for Image DenoisingCode0
Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial NetworkCode0
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural networkCode0
Solving RED with Weighted Proximal MethodsCode0
Focusing on what to decode and what to train: SOV Decoding with Specific Target Guided DeNoising and Vision Language AdvisorCode0
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source SeparationCode0
Regularization by Denoising: Clarifications and New InterpretationsCode0
Denoising of MR images with Rician noise using a wider neural network and noise range divisionCode0
Path-Restore: Learning Network Path Selection for Image RestorationCode0
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language ModelCode0
Conditional Diffusion Models with Classifier-Free Gibbs-like GuidanceCode0
Patronus: Bringing Transparency to Diffusion Models with PrototypesCode0
SPINE: SParse Interpretable Neural EmbeddingsCode0
Joint Visual Denoising and Classification using Deep LearningCode0
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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