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 4401–4425 of 7282 papers

TitleStatusHype
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model—0
The G-invariant graph Laplacian—0
Improving J-divergence of brain connectivity states by graph Laplacian denoising—0
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising—0
A Directed Graph Fourier Transform with Spread Frequency Components—0
Improving Neural Machine Translation by Denoising Training—0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network—0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising—0
Position Bias Estimation with Item Embedding for Sparse Dataset—0
The jump set under geometric regularisation. Part 2: Higher-order approaches—0
Improving Reproducibility and Performance of Radiomics in Low Dose CT using Cycle GANs—0
ADIR: Adaptive Diffusion for Image Reconstruction—0
Improving the quality control of seismic data through active learning—0
Improving the spatial resolution of a BOTDA sensor using deconvolution algorithm—0
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder—0
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders—0
Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach—0
Impulsive noise removal from color images with morphological filtering—0
Inception-Residual Block based Neural Network for Thermal Image Denoising—0
The jump set under geometric regularisation. Part 1: Basic technique and first-order denoising—0
Incomplete Multi-view Clustering via Diffusion Contrastive Generation—0
In-context denoising with one-layer transformers: connections between attention and associative memory retrieval—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—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