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 6426–6450 of 7282 papers

TitleStatusHype
CURL: Neural Curve Layers for Global Image EnhancementCode0
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse ProblemsCode0
GanLM: Encoder-Decoder Pre-training with an Auxiliary DiscriminatorCode0
A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal TractCode0
RENOIR - A Dataset for Real Low-Light Image Noise ReductionCode0
A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT DenoisingCode0
Task-Oriented Low-Dose CT Image DenoisingCode0
A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image DenoisingCode0
Gated Orthogonal Recurrent Units: On Learning to ForgetCode0
Deep Graph Laplacian Regularization for Robust Denoising of Real ImagesCode0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
Unsupervised dynamic modeling of medical image transformationCode0
Gaussian Gated Linear NetworksCode0
Unsupervised classification of the spectrogram zerosCode0
Using Deep LSD to build operators in GANs latent space with meaning in real spaceCode0
Educating Text Autoencoders: Latent Representation Guidance via DenoisingCode0
A denoised Mean Teacher for domain adaptive point cloud registrationCode0
Active Generation for Image ClassificationCode0
Phase-aware Single-stage Speech Denoising and Dereverberation with U-NetCode0
Stable Consistency Tuning: Understanding and Improving Consistency ModelsCode0
Multiscale Sparsifying Transform Learning for Image DenoisingCode0
Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion SamplingCode0
Multi-Scale Texture Loss for CT denoising with GANsCode0
Differentiable programming for functional connectomicsCode0
Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker VerificationCode0
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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