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 2501–2525 of 7282 papers

TitleStatusHype
Distributed Machine Learning with Sparse Heterogeneous Data—0
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
Audio Classical Composer Identification by Deep Neural Network—0
Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising—0
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism—0
Audio Attacks and Defenses against AED Systems -- A Practical Study—0
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing—0
Exploring the Efficacy of Partial Denoising Using Bit Plane Slicing for Enhanced Fracture Identification: A Comparative Study of Deep Learning-Based Approaches and Handcrafted Feature Extraction Techniques—0
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal—0
DeBaTeR: Denoising Bipartite Temporal Graph for Recommendation—0
AI-Driven Segmentation and Analysis of Microbial Cells—0
Exploring the latent space of diffusion models directly through singular value decomposition—0
DeBaRA: Denoising-Based 3D Room Arrangement Generation—0
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
A two-stage learning method for protein-protein interaction prediction—0
Observer-based switched-linear system identification—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
Exploring User-level Gradient Inversion with a Diffusion Prior—0
Exposing the Fake: Effective Diffusion-Generated Images Detection—0
DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration—0
A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement—0
DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models—0
A-IDE : Agent-Integrated Denoising Experts—0
Exploring Strategies for Personalized Radiation Therapy Part II Predicting Tumor Drift Patterns with Diffusion Models—0
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion—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