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 6651–6675 of 7282 papers

TitleStatusHype
On Nearest Neighbors in Non Local Means Denoising—0
xUnit: Learning a Spatial Activation Function for Efficient Image RestorationCode0
Optimal Combination of Image Denoisers—0
Sparse High-Dimensional Linear Regression. Algorithmic Barriers and a Local Search Algorithm—0
Unsupervised patient representations from clinical notes with interpretable classification decisions—0
Evaluating deep variational autoencoders trained on pan-cancer gene expression—0
Denoising Imaging Polarimetry by an Adapted BM3D Method—0
Improving Hypernymy Extraction with Distributional Semantic ClassesCode0
Statistical evaluation of visual quality metrics for image denoising—0
Sparse-View X-Ray CT Reconstruction Using _1 Prior with Learned Transform—0
Learned Convolutional Sparse CodingCode0
多樣訊雜比之訓練語料於降噪自動編碼器其語音強化功能之初步研究 (A Preliminary Study of Various SNR-level Training Data in the Denoising Auto-encoder (DAE) Technique for Speech Enhancement) [In Chinese]—0
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification—0
Denoising random forests—0
On the Taut String Interpretation of the One-dimensional Rudin-Osher-Fatemi Model: A New Proof, a Fundamental Estimate and Some Applications—0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition—0
On denoising modulo 1 samples of a function—0
Multimodal Autoencoder: A Deep Learning Approach to Filling In Missing Sensor Data and Enabling Better Mood PredictionCode0
Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks—0
Artifact reduction for separable non-local means—0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random FieldCode0
Image Restoration by Iterative Denoising and Backward ProjectionsCode1
A Review of Convolutional Neural Networks for Inverse Problems in ImagingCode0
FFDNet: Toward a Fast and Flexible Solution for CNN based Image DenoisingCode0
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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