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

Showing 26762700 of 7282 papers

TitleStatusHype
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationCode0
Classifier and Exemplar Synthesis for Zero-Shot LearningCode0
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and DecodingCode0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
Gated Orthogonal Recurrent Units: On Learning to ForgetCode0
Gaussian Gated Linear NetworksCode0
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow FieldsCode0
Diffusion Models for Computational Neuroimaging: A SurveyCode0
Class-Aware Fully-Convolutional Gaussian and Poisson DenoisingCode0
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient NetworkCode0
GanLM: Encoder-Decoder Pre-training with an Auxiliary DiscriminatorCode0
Diffusion Models as Masked AutoencodersCode0
Game Theory for Adversarial Attacks and DefensesCode0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Convolutional Dictionary Learning: Acceleration and ConvergenceCode0
Fully Unsupervised Probabilistic Noise2VoidCode0
A Self-Supervised Method for Attenuating Seismic Random and Tracewise Coherent Noise under the Non-Pixelwise Independence AssumptionCode0
Multi-head Sequence Tagging Model for Grammatical Error CorrectionCode0
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic SegmentationCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion ModelsCode0
Evaluating Unsupervised Denoising Requires Unsupervised MetricsCode0
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy ImagesCode0
Generalized Octave Convolutions for Learned Multi-Frequency Image CompressionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified