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 5026–5050 of 7282 papers

TitleStatusHype
Del-Net: A Single-Stage Network for Mobile Camera ISP—0
Simultaneous reconstruction and displacement estimation for spectral-domain optical coherence elastography—0
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential EquationsCode2
Multilingual Translation from Denoising Pre-Training—0
NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification—0
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising—0
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text—0
Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation—0
PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning—0
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-TuningCode0
CERL: A Unified Optimization Framework for Light Enhancement with Realistic NoiseCode1
DCT2net: an interpretable shallow CNN for image denoisingCode1
Observer-based switched-linear system identification—0
Data-driven modeling of time-domain induced polarizationCode0
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement—0
Self-Supervised Inference in State-Space Models—0
Adaptive Denoising via GainTuning—0
Denoising and Segmentation of Epigraphical Scripts—0
A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS—0
Utilizing the Structure of the Curvelet Transform with Compressed Sensing—0
Discrete Denoising Flows—0
Score-Based Point Cloud DenoisingCode1
Large-scale graph representation learning with very deep GNNs and self-supervision—0
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection—0
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages—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