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 6001–6025 of 7282 papers

TitleStatusHype
Spatial-Frequency Domain Nonlocal Total Variation for Image Denoising—0
Observer-based switched-linear system identification—0
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
DeBaRA: Denoising-Based 3D Room Arrangement Generation—0
DeBaTeR: Denoising Bipartite Temporal Graph for Recommendation—0
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism—0
Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising—0
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
Distributed Machine Learning with Sparse Heterogeneous Data—0
Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition—0
Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models—0
Noise reduction for weak lensing mass mapping: An application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data—0
Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts—0
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising—0
Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement—0
Decomposition Ascribed Synergistic Learning for Unified Image Restoration—0
Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes—0
DeCompress: Denoising via Neural Compression—0
Analysing Diffusion Segmentation for Medical Images—0
Deconstructing Principal Component Analysis Using a Data Reconciliation Perspective—0
Deconstructing the Ladder Network Architecture—0
VBD-MT Chinese-Vietnamese Translation Systems for VLSP 2022—0
VCD-Texture: Variance Alignment based 3D-2D Co-Denoising for Text-Guided Texturing—0
Decoupled Diffusion Sparks Adaptive Scene Generation—0
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning—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