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

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Showing 2901–2950 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
DexDiffuser: Generating Dexterous Grasps with Diffusion Models—0
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models—0
An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis—0
Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms—0
Bridge the Gap between SNN and ANN for Image Restoration—0
DEVDAN: Deep Evolving Denoising Autoencoder—0
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking—0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition—0
A Novel DDPM-based Ensemble Approach for Energy Theft Detection in Smart Grids—0
Brick-Diffusion: Generating Long Videos with Brick-to-Wall Denoising—0
Bregman Plug-and-Play Priors—0
An Effective Image Restorer: Denoising and Luminance Adjustment for Low-photon-count Imaging—0
Detection of blue whale vocalisations using a temporal-domain convolutional neural network—0
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space—0
Bregman Iteration for Correspondence Problems: A Study of Optical Flow—0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation—0
BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis—0
An Effective Fusion Method to Enhance the Robustness of CNN—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising—0
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction—0
Brain-Driven Representation Learning Based on Diffusion Model—0
BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment—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