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 876–900 of 7282 papers

TitleStatusHype
Object-aware Inversion and Reassembly for Image EditingCode1
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework DesignCode1
Provable Probabilistic Imaging using Score-Based Generative PriorsCode1
A cross Transformer for image denoisingCode1
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image DenoisingCode1
Physics-guided Noise Neural Proxy for Practical Low-light Raw Image DenoisingCode1
Denoising Task Routing for Diffusion ModelsCode1
Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion ModelCode1
Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging NoiseCode1
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion ProcessesCode1
Generative quantum machine learning via denoising diffusion probabilistic modelsCode1
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food DetectionCode1
Denoising Diffusion Step-aware ModelsCode1
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
Predictive microstructure image generation using denoising diffusion probabilistic modelsCode1
Generalization in diffusion models arises from geometry-adaptive harmonic representationsCode1
On Memorization in Diffusion ModelsCode1
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven PriorsCode1
Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion modelCode1
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform GenerationCode1
Diffusion Posterior Illumination for Ambiguity-aware Inverse RenderingCode1
High Perceptual Quality Wireless Image Delivery with Denoising Diffusion ModelsCode1
On the Posterior Distribution in Denoising: Application to Uncertainty QuantificationCode1
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-ThoughtCode1
Score Mismatching for Generative ModelingCode1
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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