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 2151–2200 of 7282 papers

TitleStatusHype
DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects—0
FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion ModelsCode0
G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis—0
Unsupervised Microscopy Video Denoising—0
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection—0
Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications—0
Factorized Diffusion: Perceptual Illusions by Noise Decomposition—0
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing PansharpeningCode1
Molecular relaxation by reverse diffusion with time step predictionCode1
Generating Human Interaction Motions in Scenes with Text Control—0
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification TasksCode0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
OneActor: Consistent Character Generation via Cluster-Conditioned Guidance—0
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models—0
WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information FusionCode1
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
Masked and Shuffled Blind Spot Denoising for Real-World Images—0
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising—0
RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via DiffusionCode1
Synthesis of Through-Wall Micro-Doppler Signatures of Human Motions Using Generative Adversarial Networks—0
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction—0
NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark DatasetCode1
Lossy Image Compression with Foundation Diffusion Models—0
Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noiseCode2
Taming Stable Diffusion for Text to 360° Panorama Image GenerationCode3
Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image DenoisingCode2
ConsistencyDet: A Few-step Denoising Framework for Object Detection Using the Consistency ModelCode0
Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification—0
Single Stage Adaptive Multi-Attention Network for Image RestorationCode0
Efficient Denoising using Score Embedding in Score-based Diffusion Models—0
Move Anything with Layered Scene Diffusion—0
GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models—0
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI—0
Adversarial purification for no-reference image-quality metrics: applicability study and new methods—0
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space—0
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework—0
scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating samplingCode1
Quantum State Generation with Structure-Preserving Diffusion Model—0
LIPT: Latency-aware Image Processing TransformerCode1
GeoDirDock: Guiding Docking Along Geodesic Paths—0
High Noise Scheduling is a Must—0
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation—0
Taming Transformers for Realistic Lidar Point Cloud GenerationCode1
Rethinking the Spatial Inconsistency in Classifier-Free Diffusion GuidanceCode1
A Training-Free Plug-and-Play Watermark Framework for Stable Diffusion—0
Wi-Fi-based Personnel Identity Recognition: Addressing Dataset Imbalance with C-DDPMs—0
Dual-Scale Transformer for Large-Scale Single-Pixel ImagingCode1
Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion ModelsCode2
Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation—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