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 4026–4050 of 7282 papers

TitleStatusHype
SeqDiffuSeq: Text Diffusion with Encoder-Decoder TransformersCode1
SimpleStyle: An Adaptable Style Transfer Approach—0
GanLM: Encoder-Decoder Pre-training with an Auxiliary DiscriminatorCode0
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network—0
Mu^2SLAM: Multitask, Multilingual Speech and Language Models—0
MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video GenerationCode2
Empowering Diffusion Models on the Embedding Space for Text GenerationCode1
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Single Frame Laser Diode Photoacoustic Imaging: Denoising and Reconstruction—0
Learning-Based Reconstruction of FRI SignalsCode0
Uncovering the Disentanglement Capability in Text-to-Image Diffusion ModelsCode1
GFPose: Learning 3D Human Pose Prior with Gradient FieldsCode1
Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic ModelsCode1
Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management—0
fMRI-based Static and Dynamic Functional Connectivity Analysis for Post-stroke Motor Dysfunction Patient: A Review—0
UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units—0
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech EnhancementCode1
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
Single Cell Training on Architecture Search for Image Denoising—0
How to Backdoor Diffusion Models?Code1
Learning to adapt unknown noise for hyperspectral image denoising—0
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising—0
Attention in a family of Boltzmann machines emerging from modern Hopfield networksCode0
Denoising Self-attentive Sequential Recommendation—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