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 2751–2775 of 7282 papers

TitleStatusHype
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina—0
FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model—0
CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion—0
Exploring Magnitude Preservation and Rotation Modulation in Diffusion Transformers—0
A Simple Yet Effective Improvement to the Bilateral Filter for Image Denoising—0
Exploring Optimal Latent Trajetory for Zero-shot Image Editing—0
Exploring Position Encoding in Diffusion U-Net for Training-free High-resolution Image Generation—0
Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models—0
Exploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling—0
Exploring Strategies for Personalized Radiation Therapy Part II Predicting Tumor Drift Patterns with Diffusion Models—0
FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts—0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
Exploring the Efficacy of Partial Denoising Using Bit Plane Slicing for Enhanced Fracture Identification: A Comparative Study of Deep Learning-Based Approaches and Handcrafted Feature Extraction Techniques—0
Exploring the latent space of diffusion models directly through singular value decomposition—0
Exploring User-level Gradient Inversion with a Diffusion Prior—0
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios—0
Cost-Aware Routing for Efficient Text-To-Image Generation—0
Exposing the Fake: Effective Diffusion-Generated Images Detection—0
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving—0
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising—0
Explorative Inbetweening of Time and Space—0
A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
A Spectrum-based Image Denoising Method with Edge Feature Enhancement—0
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness—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