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 2426–2450 of 7282 papers

TitleStatusHype
Image Reconstruction with Predictive Filter FlowCode0
Image Embedding for Denoising Generative ModelsCode0
Image Denoising with Graph-Convolutional Neural NetworksCode0
Image Fusion via Sparse Regularization with Non-Convex PenaltiesCode0
Approximate Bayesian Computation with the Sliced-Wasserstein DistanceCode0
Image Denoising with Control over Deep Network HallucinationCode0
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
Image denoising using complex-valued deep CNNCode0
Image denoising using deep CNN with batch renormalizationCode0
Compression Artifacts Reduction by a Deep Convolutional NetworkCode0
Image Blind Denoising Using Dual Convolutional Neural Network with Skip ConnectionCode0
Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast MammographyCode0
Dual Caption Preference Optimization for Diffusion ModelsCode0
IIDM: Image-to-Image Diffusion Model for Semantic Image SynthesisCode0
IFH: a Diffusion Framework for Flexible Design of Graph Generative ModelsCode0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
Identity Enhanced Residual Image DenoisingCode0
Imaging at the quantum limit with convolutional neural networksCode0
Dynamic Feature Acquisition Using Denoising AutoencodersCode0
Compressed Sensing: A Discrete Optimization ApproachCode0
Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic ForgettingCode0
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in HistopathologyCode0
Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image PriorCode0
Composite Reward Design in PPO-Driven Adaptive FilteringCode0
Hyperspectral Mixed Noise Removal By L1-Norm-Based Subspace RepresentationCode0
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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