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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4276–4300 of 7282 papers

TitleStatusHype
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation—0
IlluSign: Illustrating Sign Language Videos by Leveraging the Attention Mechanism—0
Texture variation adaptive image denoising with nonlocal PCA—0
ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising—0
Image Analysis Using a Dual-Tree M-Band Wavelet Transform—0
TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance—0
Image Blind Denoising With Generative Adversarial Network Based Noise Modeling—0
Image-Conditional Diffusion Transformer for Underwater Image Enhancement—0
Multiscale Structure Guided Diffusion for Image Deblurring—0
Zero-shot Blind Image Denoising via Implicit Neural Representations—0
Image Deconvolution with Deep Image and Kernel Priors—0
Image Denoising and Inpainting with Deep Neural Networks—0
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
AutoEncoders for Training Compact Deep Learning RF Classifiers for Wireless Protocols—0
Image denoising as a conditional expectation—0
Image denoising based on improved data-driven sparse representation—0
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches—0
Image denoising by Super Neurons: Why go deep?—0
Image Denoising for Strong Gaussian Noises With Specialized CNNs for Different Frequency Components—0
Image denoising in acoustic field microscopy—0
Image Denoising in FPGA using Generic Risk Estimation—0
Image Denoising Inspired by Quantum Many-Body physics—0
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --—0
Image denoising through bivariate shrinkage function in framelet domain—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