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 3501–3525 of 7282 papers

TitleStatusHype
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Image Blind Denoising With Generative Adversarial Network Based Noise Modeling—0
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network—0
Multiscale Structure Guided Diffusion for Image Deblurring—0
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus—0
Image Deconvolution with Deep Image and Kernel Priors—0
Image Denoising and Inpainting with Deep Neural Networks—0
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method—0
Image denoising as a conditional expectation—0
Image denoising based on improved data-driven sparse representation—0
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches—0
Image denoising by Super Neurons: Why go deep?—0
Image Denoising for Strong Gaussian Noises With Specialized CNNs for Different Frequency Components—0
Image denoising in acoustic field microscopy—0
Image Denoising in FPGA using Generic Risk Estimation—0
Image Denoising Inspired by Quantum Many-Body physics—0
Information Theoretic Text-to-Image Alignment—0
INFP: Audio-Driven Interactive Head Generation in Dyadic Conversations—0
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing—0
Image Denoising using Attention-Residual Convolutional Neural Networks—0
Image Denoising Using Convolutional Autoencoder—0
Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing—0
Enhanced 3D Generation by 2D Editing—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