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 1626–1650 of 7282 papers

TitleStatusHype
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion ModelCode1
Reconciling Stochastic and Deterministic Strategies for Zero-shot Image Restoration using Diffusion Model in DualCode1
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain PackingCode1
Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image DenoisingCode1
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging LoadCode1
Empowering Diffusion Models on the Embedding Space for Text GenerationCode1
DiffO: Single-step Diffusion for Image Compression at Ultra-Low BitratesCode1
Remix-DiT: Mixing Diffusion Transformers for Multi-Expert DenoisingCode1
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic ModelsCode1
Diffusion Model for Dense MatchingCode1
DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View SynthesisCode1
DiffStyler: Diffusion-based Localized Image Style TransferCode1
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEGCode1
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech EnhancementCode1
Representation Learning for Resource-Constrained Keyphrase GenerationCode1
Differentiable Manifold Reconstruction for Point Cloud DenoisingCode1
Denoising Normalizing FlowCode1
Accelerated MRI with Un-trained Neural NetworksCode1
ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to SpeechCode1
A deep network for sinogram and CT image reconstructionCode1
DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image ClarityCode1
Rethinking Deep Image Prior for DenoisingCode1
Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional NetworksCode1
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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