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 5376–5400 of 7282 papers

TitleStatusHype
Denoising Score Matching with Random Fourier Features—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty—0
Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention FusionCode0
DENet: a deep architecture for audio surveillance applicationsCode1
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
Generate Natural Language Explanations for Recommendation—0
Hyperspectral image denoising based on global and non-local low-rank factorizationsCode1
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy ImagesCode1
Knowledge Distillation in Iterative Generative Models for Improved Sampling SpeedCode1
Interspeech 2021 Deep Noise Suppression ChallengeCode0
Contextual colorization and denoising for low-light ultra high resolution sequences—0
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data—0
Progressive Correspondence Pruning by Consensus LearningCode1
Quaternion higher-order singular value decomposition and its applications in color image processing—0
The Benefit of Distraction: Denoising Camera-Based Physiological Measurements Using Inverse Attention—0
Cross-Patch Graph Convolutional Network for Image Denoising—0
Hyperspectral Image Denoising With Realistic DataCode1
Self-Supervised Image Prior Learning With GMM From a Single Noisy ImageCode0
Self-supervised Bayesian Deep Learning for Image Denoising—0
A Simple Sparse Denoising Layer for Robust Deep Learning—0
An Unsupervised Deep Learning Approach for Real-World Image DenoisingCode1
Efficient randomized smoothing by denoising with learned score function—0
Differentiable Programming for Piecewise Polynomial Functions—0
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising—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