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 4501–4525 of 7282 papers

TitleStatusHype
Compressive Sensing with Tensorized Autoencoder—0
Blind2Sound: Self-Supervised Image Denoising without Residual Noise—0
Invertible Kernel PCA with Random Fourier FeaturesCode0
Robust Multimodal Fusion for Human Activity Recognition—0
A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments—0
TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation—0
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers—0
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN—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
Improving Audio-Visual Video Parsing with Pseudo Visual Labels—0
Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT—0
Dataset Creation Pipeline for Camera-Based Heart Rate Estimation—0
Error mitigation of entangled states using brainbox quantum autoencoders—0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption—0
Denoising-based UNMT is more robust to word-order divergence than MASS-based UNMT—0
Mixture of Soft Prompts for Controllable Data GenerationCode0
Diffusion Probabilistic Fields—0
Cloud K-SVD for Image DenoisingCode0
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
Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods—0
Monocular Depth Estimation using Diffusion Models—0
Spatial-Frequency Attention for Image Denoising—0
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse SignalsCode0
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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