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 4251–4275 of 7282 papers

TitleStatusHype
OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving—0
OccludeNeRF: Geometric-aware 3D Scene Inpainting with Collaborative Score Distillation in NeRF—0
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement—0
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations—0
ODPG: Outfitting Diffusion with Pose Guided Condition—0
O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions—0
Offloading Deep Learning Powered Vision Tasks from UAV to 5G Edge Server with Denoising—0
Adaptive Denoising of Signals with Local Shift-Invariant Structure—0
Adaptive Cyclic Diffusion for Inference Scaling—0
Adaptive Cohen's Class Time-Frequency Distribution—0
OmniCreator: Self-Supervised Unified Generation with Universal Editing—0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
On 1-Laplacian Elliptic Equations Modeling Magnetic Resonance Image Rician Denoising—0
OnACID: Online Analysis of Calcium Imaging Data in Real Time—0
On a Mechanism Framework of Autoencoders—0
On a new formulation of nonlocal image filters involving the relative rearrangement—0
On a non-local spectrogram for denoising one-dimensional signals—0
On approximating f with neural networks—0
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation—0
TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain—0
On change point detection using the fused lasso method—0
On Convergent Finite Difference Schemes for Variational - PDE Based Image Processing—0
Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction—0
On denoising autoencoders trained to minimise binary cross-entropy—0
On denoising modulo 1 samples of a function—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