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 2901–2925 of 7282 papers

TitleStatusHype
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction—0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
Dictionary Learning with Equiprobable Matching Pursuit—0
Dictionary Learning Under Generative Coefficient Priors with Applications to Compression—0
An ELU Network with Total Variation for Image Denoising—0
Dictionary Learning Based on Sparse Distribution Tomography—0
Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis—0
Bring the Power of Diffusion Model to Defect Detection—0
DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction—0
Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition—0
An Efficient Statistical Method for Image Noise Level Estimation—0
Diagnostic Quality Assessment for Low-Dimensional ECG Representations—0
Bring Metric Functions into Diffusion Models—0
Diagnosing and Preventing Instabilities in Recurrent Video Processing—0
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models—0
DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding—0
D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples—0
Brightness-Invariant Tracking Estimation in Tagged MRI—0
Bright-NeRF:Brightening Neural Radiance Field with Color Restoration from Low-light Raw Images—0
A Decoupled Learning Scheme for Real-world Burst Denoising from Raw Images—0
Acceleration of the PDHGM on strongly convex subspaces—0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids—0
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding—0
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement—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