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

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Showing 3701–3725 of 7282 papers

TitleStatusHype
Exploring Position Encoding in Diffusion U-Net for Training-free High-resolution Image Generation—0
Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models—0
Exploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling—0
Exploring Strategies for Personalized Radiation Therapy Part II Predicting Tumor Drift Patterns with Diffusion Models—0
SW-ViT: A Spatio-Temporal Vision Transformer Network with Post Denoiser for Sequential Multi-Push Ultrasound Shear Wave Elastography—0
VIP: Video Inpainting Pipeline for Real World Human Removal—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
Exploring the latent space of diffusion models directly through singular value decomposition—0
Exploring User-level Gradient Inversion with a Diffusion Prior—0
Exposing the Fake: Effective Diffusion-Generated Images Detection—0
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising—0
SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions—0
To smooth a cloud or to pin it down: Guarantees and Insights on Score Matching in Denoising Diffusion Models—0
ExScene: Free-View 3D Scene Reconstruction with Gaussian Splatting from a Single Image—0
A Generative Adversarial Approach with Residual Learning for Dust and Scratches Artifacts Removal—0
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation—0
Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation—0
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling—0
Extensions to the Proximal Distance Method of Constrained Optimization—0
External Knowledge Enhanced 3D Scene Generation from Sketch—0
External Patch Prior Guided Internal Clustering for Image Denoising—0
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising—0
Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration—0
Extracting Information-rich Part of Texts using Text Denoising—0
Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey—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