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 5551–5600 of 7282 papers

TitleStatusHype
Blind Image Restoration with Flow Based Priors—0
Modified Weibull distribution for Biomedical signals denoising—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising—0
BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers—0
Block-Matching Convolutional Neural Network for Image Denoising—0
Block-matching in FPGA—0
Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks—0
Block-wise Adaptive Caching for Accelerating Diffusion Policy—0
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model—0
Blockwise SURE Shrinkage for Non-Local Means—0
Blurring Diffusion Models—0
BM3D vs 2-Layer ONN—0
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain—0
BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS—0
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion—0
Boosting Image Restoration via Priors from Pre-trained Models—0
Boosting of Image Denoising Algorithms—0
SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes—0
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping—0
Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data—0
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement—0
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog—0
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images—0
An Empirical Study of ADMM for Nonconvex Problems—0
BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment—0
Brain-Driven Representation Learning Based on Diffusion Model—0
BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis—0
Bregman Iteration for Correspondence Problems: A Study of Optical Flow—0
Bregman Plug-and-Play Priors—0
Brick-Diffusion: Generating Long Videos with Brick-to-Wall Denoising—0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition—0
Bridge the Gap between SNN and ANN for Image Restoration—0
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models—0
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding—0
Bright-NeRF:Brightening Neural Radiance Field with Color Restoration from Low-light Raw Images—0
Brightness-Invariant Tracking Estimation in Tagged MRI—0
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models—0
Bring Metric Functions into Diffusion Models—0
Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition—0
Bring the Power of Diffusion Model to Defect Detection—0
Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches—0
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text—0
BudgetFusion: Perceptually-Guided Adaptive Diffusion Models—0
BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models—0
An ELU Network with Total Variation for Image 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