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 2951–2975 of 7282 papers

TitleStatusHype
Anisotropic Diffusion Probabilistic Model for Imbalanced Image Classification—0
Quantitative and Qualitative Evaluation of NLM and Wavelet Methods in Image Enhancement—0
High-dimensional learning of narrow neural networks—0
PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models—0
Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier—0
DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention—0
Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation—0
When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising RecommendationCode0
Unrolled denoising networks provably learn optimal Bayesian inference—0
Denoising diffusion models for high-resolution microscopy image restoration—0
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation—0
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis—0
Noise-aware Dynamic Image Denoising and Positron Range Correction for Rubidium-82 Cardiac PET Imaging via Self-supervision—0
Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models—0
Edge-based Denoising Image Compression—0
Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models—0
Taming Diffusion Models for Image Restoration: A Review—0
Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging—0
BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy MatchingCode0
Schrödinger Bridge Flow for Unpaired Data Translation—0
ManiDext: Hand-Object Manipulation Synthesis via Continuous Correspondence Embeddings and Residual-Guided Diffusion—0
E1 TTS: Simple and Fast Non-Autoregressive TTS—0
Robust Training of Neural Networks at Arbitrary Precision and Sparsity—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