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 5526–5550 of 7282 papers

TitleStatusHype
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)—0
Bilingual-GAN: A Step Towards Parallel Text Generation—0
AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies—0
Binary Diffusion Probabilistic Model—0
BioAtt: Anatomical Prior Driven Low-Dose CT Denoising—0
Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising—0
Biologically Inspired Radio Signal Feature Extraction with Sparse Denoising Autoencoders—0
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys—0
Single-View Height Estimation with Conditional Diffusion Probabilistic Models—0
AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse—0
Variational Denoising for Variational Quantum Eigensolver—0
Bitwise Operations of Cellular Automaton on Gray-scale Images—0
BJTU-WeChat's Systems for the WMT22 Chat Translation Task—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Blind2Sound: Self-Supervised Image Denoising without Residual Noise—0
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography—0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation—0
Blind Biological Sequence Denoising with Self-Supervised Set Learning—0
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach—0
Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator—0
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations—0
Blind Denoising Autoencoder—0
SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression—0
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches—0
Blind Image Denoising via Dependent Dirichlet Process Tree—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