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 5651–5675 of 7282 papers

TitleStatusHype
Channel Estimation for Large Intelligent Surface Aided MISO Communications: From LMMSE to Deep Learning Solutions—0
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach—0
Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach—0
Channel Phase Processing in Wireless Networks for Human Activity Recognition—0
Statistical and Computational Efficiency for Smooth Tensor Estimation with Unknown Permutations—0
A decomposition of book structure through ousiometric fluctuations in cumulative word-time—0
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence—0
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising—0
Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation—0
CHIME: Conditional Hallucination and Integrated Multi-scale Enhancement for Time Series Diffusion Model—0
SMPLR: Deep SMPL reverse for 3D human pose and shape recovery—0
ChiroDiff: Modelling chirographic data with Diffusion Models—0
CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images—0
CIIDefence: Defeating Adversarial Attacks by Fusing Class-Specific Image Inpainting and Image Denoising—0
CIM-NET: A Video Denoising Deep Neural Network Model Optimized for Computing-in-Memory Architectures—0
Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model—0
SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder—0
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors—0
Class-Aware Contrastive Optimization for Imbalanced Text Classification—0
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds—0
Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable—0
Classification-Denoising Networks—0
Classification Diffusion Models: Revitalizing Density Ratio Estimation—0
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising—0
Classifier-Free Guidance is a Predictor-Corrector—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