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 3901–3925 of 7282 papers

TitleStatusHype
Cross-Image Attention for Zero-Shot Appearance Transfer—0
Robust Generalization Strategies for Morpheme Glossing in an Endangered Language Documentation Context—0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Thermal Face Image Classification using Deep Learning Techniques—0
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation—0
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method—0
CDGraph: Dual Conditional Social Graph Synthesizing via Diffusion Model—0
Add and Thin: Diffusion for Temporal Point Processes—0
Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction—0
POS: A Prompts Optimization Suite for Augmenting Text-to-Video Generation—0
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models—0
Towards High-quality HDR Deghosting with Conditional Diffusion Models—0
Robust Graph Clustering via Meta Weighting for Noisy GraphsCode0
LatentWarp: Consistent Diffusion Latents for Zero-Shot Video-to-Video Translation—0
The Missing U for Efficient Diffusion Models—0
Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward AlignmentCode0
Medical Image Denosing via Explainable AI Feature Preserving Loss—0
Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty—0
DPATD: Dual-Phase Audio Transformer for Denoising—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications—0
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications—0
DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification—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