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 3701–3750 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
Towards a Precipitation Bias Corrector against Noise and Maldistribution—0
Low-Dose CT Image Denoising Using Parallel-Clone Networks—0
Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers—0
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20 Speedup—0
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning—0
Low Latency Video Denoising for Online Conferencing Using CNN Architectures—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond—0
Low power communication signal enhancement method of Internet of things based on nonlocal mean denoising—0
Low-rank matrix completion and denoising under Poisson noise—0
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising—0
Low-rankness of Complex-valued Spectrogram and Its Application to Phase-aware Audio Processing—0
Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising—0
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery—0
Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising—0
LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising—0
LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images—0
LSD_2 -- Joint Denoising and Deblurring of Short and Long Exposure Images with CNNs—0
Addressing Negative Transfer in Diffusion Models—0
LuminanceL1Loss: A loss function which measures percieved brightness and colour differences—0
Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology—0
L-WaveBlock: A Novel Feature Extractor Leveraging Wavelets for Generative Adversarial Networks—0
LW-ISP: A Lightweight Model with ISP and Deep Learning—0
M2D2M: Multi-Motion Generation from Text with Discrete Diffusion Models—0
Towards Balanced Continual Multi-Modal Learning in Human Pose Estimation—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