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 4326–4350 of 7282 papers

TitleStatusHype
Image Inpainting Using Directional Tensor Product Complex Tight Framelets—0
Image Processing Tools for Financial Time Series Classification—0
A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images—0
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers—0
The Benefit of Distraction: Denoising Camera-Based Physiological Measurements Using Inverse Attention—0
Image Reconstruction Using Deep Learning—0
Image Reconstruction using Enhanced Vision Transformer—0
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors—0
The Benefit of Distraction: Denoising Remote Vitals Measurements using Inverse Attention—0
The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Random Access in Cellular IoT—0
Image Restoration using Autoencoding Priors—0
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians—0
Image Restoration using Total Variation Regularized Deep Image Prior—0
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks—0
Image Restoration with Locally Selected Class-Adapted Models—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
The conjugated null space method of blind PSF estimation and deconvolution optimization—0
Image Segmentation and Restoration Using Parametric Contours With Free Endpoints—0
Image Segmentation Using Overlapping Group Sparsity—0
Image Speckle Noise Denoising by a Multi-Layer Fusion Enhancement Method based on Block Matching and 3D Filtering—0
Sparse Solutions of a Class of Constrained Optimization Problems—0
Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved—0
Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning—0
The Deep Kernelized Autoencoder—0
Imagine-2-Drive: Leveraging High-Fidelity World Models via Multi-Modal Diffusion Policies—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