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 2326–2350 of 7282 papers

TitleStatusHype
Enhanced Low-Rank Matrix Approximation—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Backdoor Attacks against Image-to-Image Networks—0
Generative Imaging and Image Processing via Generative Encoder—0
Deep Manifold Prior—0
Deeply Coupled Auto-encoder Networks for Cross-view Classification—0
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior—0
Deeply Cascaded U-Net for Multi-Task Image Processing—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Aligning Protein Conformation Ensemble Generation with Physical Feedback—0
Adaptive Domain Learning for Cross-domain Image Denoising—0
Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models—0
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19—0
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature—0
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders—0
Deep Learning on Image Denoising: An overview—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
Deep-Learning Inversion Method for the Interpretation of Noisy Logging-While-Drilling Resistivity Measurements—0
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images—0
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging—0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction—0
Enhanced Denoising and Convergent Regularisation Using Tweedie Scaling—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