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 6651–6700 of 7282 papers

TitleStatusHype
Burst Denoising with Kernel Prediction NetworksCode0
Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification—0
RF-Based Direction Finding of UAVs Using DNNCode0
InverseNet: Solving Inverse Problems with Splitting Networks—0
OnACID: Online Analysis of Calcium Imaging Data in Real Time—0
Properties on n-dimensional convolution for image deconvolution—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders—0
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery—0
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders—0
SPINE: SParse Interpretable Neural EmbeddingsCode0
A Face Fairness Framework for 3D Meshes—0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising—0
On Nearest Neighbors in Non Local Means Denoising—0
Optimal Combination of Image Denoisers—0
xUnit: Learning a Spatial Activation Function for Efficient Image RestorationCode0
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
Sparse-View X-Ray CT Reconstruction Using _1 Prior with Learned Transform—0
Statistical evaluation of visual quality metrics for image denoising—0
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
Learned Convolutional Sparse CodingCode0
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
Artifact reduction for separable non-local means—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
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
FFDNet: Toward a Fast and Flexible Solution for CNN based Image DenoisingCode0
A Review of Convolutional Neural Networks for Inverse Problems in ImagingCode0
Iterative PET Image Reconstruction Using Convolutional Neural Network RepresentationCode0
Reconstruction of Hidden Representation for Robust Feature Extraction—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
Video Denoising and Enhancement via Dynamic Video Layering—0
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video DenoisingCode0
Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITKCode0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Predictor Combination at Test Time—0
Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video DenoisingCode0
Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising—0
A Variational Approach to Shape-from-shading Under Natural Illumination—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