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

Showing 17261750 of 7282 papers

TitleStatusHype
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language ModelsCode1
PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-ResolutionCode1
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising AutoencodersCode1
A Neural Network for SemigroupsCode0
MicroSSIM: Improved Structural Similarity for Comparing Microscopy DataCode0
Cached Adaptive Token Merging: Dynamic Token Reduction and Redundant Computation Elimination in Diffusion ModelCode0
Microscopy Image Restoration with Deep Wiener-Kolmogorov filtersCode0
Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser NetworkCode0
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory PredictionCode0
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform InitializationCode0
Burst Denoising with Kernel Prediction NetworksCode0
An End-to-End Compression Framework Based on Convolutional Neural NetworksCode0
MFM-DA: Instance-Aware Adaptor and Hierarchical Alignment for Efficient Domain Adaptation in Medical Foundation ModelsCode0
MFTF: Mask-free Training-free Object Level Layout Control Diffusion ModelCode0
Missing Data Imputation with Adversarially-trained Graph Convolutional NetworksCode0
An Encoder-Decoder Approach to the Paradigm Cell Filling ProblemCode0
Mesh Denoising with Facet Graph ConvolutionsCode0
Building 3D In-Context Learning Universal Model in NeuroimagingCode0
An empirical study on the effects of different types of noise in image classification tasksCode0
Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-ExplorationCode0
Multi-Stage Speaker Diarization for Noisy ClassroomsCode0
BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised EmbeddingCode0
An Empirical Review of Adversarial DefensesCode0
Metaphor Detection with Effective Context DenoisingCode0
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial NetworkCode0
Show:102550
← PrevPage 70 of 292Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified