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 3026–3050 of 7282 papers

TitleStatusHype
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
Denoising random forests—0
Block-wise Adaptive Caching for Accelerating Diffusion Policy—0
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor—0
Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks—0
An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detection—0
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers—0
Block-matching in FPGA—0
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation—0
Block-Matching Convolutional Neural Network for Image Denoising—0
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation—0
Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models—0
Denoising Opponents Position in Partial Observation Environment—0
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network—0
Denoising of photogrammetric dummy head ear point clouds for individual Head-Related Transfer Functions computation—0
Analyzing noise in autoencoders and deep networks—0
Denoising of image gradients and total generalized variation denoising—0
Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction—0
Denoising of discrete-time chaotic signals using echo state networks—0
BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers—0
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization—0
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems—0
Accelerating Image Generation with Sub-path Linear Approximation Model—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