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 3701–3725 of 7282 papers

TitleStatusHype
Local Curvature Smoothing with Stein's Identity for Efficient Score Matching—0
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy—0
Localized Schrödinger Bridge Sampler—0
Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison (Extended Cut)—0
Towards a Mechanistic Explanation of Diffusion Model Generalization—0
Complex-valued Retrievals From Noisy Images Using Diffusion Models—0
Local Sparse Approximation for Image Restoration with Adaptive Block Size Selection—0
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation—0
A Decoupled Learning Scheme for Real-world Burst Denoising from Raw Images—0
LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising—0
Lookahead optimizer improves the performance of Convolutional Autoencoders for reconstruction of natural images—0
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering—0
LoRA-Enhanced Distillation on Guided Diffusion Models—0
LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers—0
LoRID: Low-Rank Iterative Diffusion for Adversarial Purification—0
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
Lossy Image Compression with Foundation Diffusion Models—0
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning—0
Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models—0
Low-dimensional adaptation of diffusion models: Convergence in total variation—0
Low-dimensional Denoising Embedding Transformer for ECG Classification—0
Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network—0
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization—0
Low-Dose CT Denoising via Sinogram Inner-Structure Transformer—0
Low-dose CT denoising with convolutional neural network—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