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 3826–3850 of 7282 papers

TitleStatusHype
Learning multi-scale local conditional probability models of imagesCode1
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers—0
How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model—0
Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive DenoisingCode0
Synthetic ECG Signal Generation using Probabilistic Diffusion ModelsCode1
Improving Audio-Visual Video Parsing with Pseudo Visual Labels—0
Unleashing Text-to-Image Diffusion Models for Visual PerceptionCode2
Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT—0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption—0
Mixture of Soft Prompts for Controllable Data GenerationCode0
Denoising-based UNMT is more robust to word-order divergence than MASS-based UNMT—0
Error mitigation of entangled states using brainbox quantum autoencoders—0
Human Motion Diffusion as a Generative PriorCode2
Dataset Creation Pipeline for Camera-Based Heart Rate Estimation—0
Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation—0
A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT—0
Cloud K-SVD for Image DenoisingCode0
Diffusion Probabilistic Fields—0
Monocular Depth Estimation using Diffusion Models—0
Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods—0
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse SignalsCode0
Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising—0
Supervised topological data analysis for MALDI mass spectrometry imaging applications—0
Denoising Diffusion Samplers—0
Spatial-Frequency Attention for Image Denoising—0
Show:102550
← PrevPage 154 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