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 1826–1850 of 7282 papers

TitleStatusHype
Blockwise SURE Shrinkage for Non-Local Means—0
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis—0
Block-wise Adaptive Caching for Accelerating Diffusion Policy—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
Block-matching in FPGA—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
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems—0
Analyzing noise in autoencoders and deep 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
Accelerating Image Generation with Sub-path Linear Approximation Model—0
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation—0
Analyzing and Improving Model Collapse in Rectified Flow Models—0
Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising—0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images—0
Analytical Discovery of Manifold with Machine Learning—0
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models—0
Analysis Operator Learning and Its Application to Image Reconstruction—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Show:102550
← PrevPage 74 of 292Next →
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