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 5551–5575 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
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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