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 4151–4175 of 7282 papers

TitleStatusHype
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network—0
Noise2Blur: Online Noise Extraction and Denoising—0
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising—0
Transformer-Based Denoising of Mechanical Vibration Signals—0
Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling—0
Transformer-based Learned Image Compression for Joint Decoding and Denoising—0
Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture—0
Transformer-Based UNet with Multi-Headed Cross-Attention Skip Connections to Eliminate Artifacts in Scanned Documents—0
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images—0
Transforming Pixels into a Masterpiece: AI-Powered Art Restoration using a Novel Distributed Denoising CNN (DDCNN)—0
Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion—0
Translating Diffusion, Wavelets, and Regularisation into Residual Networks—0
Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising—0
Noise2Score3D:Unsupervised Tweedie's Approach for Point Cloud Denoising—0
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals—0
Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images—0
Transport Analysis of Infinitely Deep Neural Network—0
Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks—0
Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image—0
Noise2Stack: Improving Image Restoration by Learning from Volumetric Data—0
Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model—0
Noise-aware Dynamic Image Denoising and Positron Range Correction for Rubidium-82 Cardiac PET Imaging via Self-supervision—0
NoiseBreaker: Gradual Image Denoising Guided by Noise Analysis—0
Adaptive Domain Learning for Cross-domain Image Denoising—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