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 4701–4725 of 7282 papers

TitleStatusHype
Quantized Neural Networks for Radar Interference Mitigation—0
Quantizing Diffusion Models from a Sampling-Aware Perspective—0
Wide-Sense Stationarity in Generalized Graph Signal Processing—0
Unleashing the Power of Large Language Model for Denoising Recommendation—0
Quantum Diffusion Models for Few-Shot Learning—0
Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble—0
Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability—0
A novel perspective on denoising using quantum localization with application to medical imaging—0
Quantum mechanics-based signal and image representation: application to denoising—0
Quantum median filter for Total Variation image denoising—0
Quantum spectral analysis: frequency in time, with applications to signal and image processing—0
Quantum State Generation with Structure-Preserving Diffusion Model—0
QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data—0
Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset—0
Quaternion higher-order singular value decomposition and its applications in color image processing—0
Quaternion Nuclear Norm minus Frobenius Norm Minimization for color image reconstruction—0
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution—0
Query-Based Single Document Summarization Using an Ensemble Noisy Auto-Encoder—0
Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
R2-Diff: Denoising by diffusion as a refinement of retrieved motion for image-based motion prediction—0
Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries—0
R3CD: Scene Graph to Image Generation with Relation-aware Compositional Contrastive Control Diffusion—0
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery—0
RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training—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