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 6751–6775 of 7282 papers

TitleStatusHype
A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising—0
DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes—0
Dynamic Slimmable Denoising Network—0
A High-Quality Denoising Dataset for Smartphone Cameras—0
Dynamic Try-On: Taming Video Virtual Try-on with Dynamic Attention Mechanism—0
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness—0
DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation—0
E1 TTS: Simple and Fast Non-Autoregressive TTS—0
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment—0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce—0
EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models—0
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling—0
SUNLayer: Stable denoising with generative networks—0
ECG Beat Representation and Delineation by means of Variable Projection—0
ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network—0
ECG Signal Denoising Using Multi-scale Patch Embedding and Transformers—0
Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration—0
Echocardiography video synthesis from end diastolic semantic map via diffusion model—0
supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination—0
A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly—0
ECNet: Effective Controllable Text-to-Image Diffusion Models—0
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference—0
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
Superkernel Neural Architecture Search for Image Denoising—0
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression—0
Show:102550
← PrevPage 271 of 292Next →
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