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 3601–3650 of 7282 papers

TitleStatusHype
Survey: Machine Learning in Production Rendering—0
Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models—0
Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise—0
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models—0
Survey of Deep Learning Methods for Inverse Problems—0
Enhancing Motion Dynamics of Image-to-Video Models via Adaptive Low-Pass Guidance—0
Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training—0
Enhancing Recommendation with Denoising Auxiliary Task—0
VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference—0
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence—0
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning—0
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models—0
Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising—0
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks—0
Survey on LiDAR Perception in Adverse Weather Conditions—0
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms—0
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing—0
Entropy stable conservative flux form neural networks—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
EP-CFG: Energy-Preserving Classifier-Free Guidance—0
Equivariant Denoisers for Image Restoration—0
Equivariant plug-and-play image reconstruction—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine—0
SVD-Based Graph Fractional Fourier Transform on Directed Graphs and Its Application—0
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix—0
EraseNet: A Recurrent Residual Network for Supervised Document Cleaning—0
ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models—0
ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application—0
ERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference—0
Error analysis for denoising smooth modulo signals on a graph—0
Error Bounds for Flow Matching Methods—0
Error Correction in ASR using Sequence-to-Sequence Models—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
Error mitigation of entangled states using brainbox quantum autoencoders—0
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—0
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems—0
SVNR: Spatially-variant Noise Removal with Denoising Diffusion—0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
Estimating High Order Gradients of the Data Distribution by Denoising—0
Estimating LASSO Risk and Noise Level—0
Estimating Post-OCR Denoising Complexity on Numerical Texts—0
Estimating User Location in Social Media with Stacked Denoising Auto-encoders—0
Sweep Distortion Removal from THz Images via Blind Demodulation—0
Epsilon-VAE: Denoising as Visual Decoding—0
Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets—0
Evaluating deep variational autoencoders trained on pan-cancer gene expression—0
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack—0
Evaluating the design space of diffusion-based generative models—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