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

Showing 11261150 of 7282 papers

TitleStatusHype
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
D3RM: A Discrete Denoising Diffusion Refinement Model for Piano TranscriptionCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
High Perceptual Quality Image Denoising with a Posterior Sampling CGANCode1
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image RestorationCode1
HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction DetectionCode1
D^4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-OnCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
DALE: Generative Data Augmentation for Low-Resource Legal NLPCode1
Dynamic Addition of Noise in a Diffusion Model for Anomaly DetectionCode1
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic ModelsCode1
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image SynthesisCode1
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric GuidanceCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
Deterministic training of generative autoencoders using invertible layersCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh ReconstructionCode1
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
CycleISP: Real Image Restoration via Improved Data SynthesisCode1
HNeRV: A Hybrid Neural Representation for VideosCode1
Clockwork Diffusion: Efficient Generation With Model-Step DistillationCode1
D3A-TS: Denoising-Driven Data Augmentation in Time SeriesCode1
Heterogeneous window transformer for image denoisingCode1
High Perceptual Quality Wireless Image Delivery with Denoising Diffusion ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified