SOTAVerified

Image Denoising

Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.

( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )

Papers

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Showing 701–750 of 1220 papers

TitleStatusHype
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling—0
Chaining Identity Mapping Modules for Image Denoising—0
Model-Based Image Signal Processors via Learnable Dictionaries—0
Modeling Graph Node Correlations with Neighbor Mixture Models—0
Thermal Face Image Classification using Deep Learning Techniques—0
The Role of Redundant Bases and Shrinkage Functions in Image Denoising—0
Moment Transform-Based Compressive Sensing in Image Processing—0
Momentum-Net for Low-Dose CT Image Reconstruction—0
Monte Carlo non local means: Random sampling for large-scale image filtering—0
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion—0
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework—0
Multi-band Weighted l_p Norm Minimization for Image Denoising—0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising—0
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising—0
MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction—0
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising—0
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images—0
CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising—0
Cascades of Regression Tree Fields for Image Restoration—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks—0
Thunder: Thumbnail based Fast Lightweight Image Denoising Network—0
Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising—0
Multimodal Image Denoising based on Coupled Dictionary Learning—0
Multimodal sparse representation learning and applications—0
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation—0
Multipoint Filtering with Local Polynomial Approximation and Range Guidance—0
Can fully convolutional networks perform well for general image restoration problems?—0
CANDID: Correspondence AligNment for Deep-burst Image Denoising—0
Multiscale Hybrid Non-local Means Filtering Using Modified Similarity Measure—0
Timbre transfer using image-to-image denoising diffusion implicit models—0
Multi-Scale Weighted Nuclear Norm Image Restoration—0
Multispectral image denoising with optimized vector non-local mean filter—0
CameraNet: A Two-Stage Framework for Effective Camera ISP Learning—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR—0
Multiview Hessian Discriminative Sparse Coding for Image Annotation—0
Boosting of Image Denoising Algorithms—0
BM3D vs 2-Layer ONN—0
Multi-wavelet residual dense convolutional neural network for image denoising—0
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture—0
Natural Adversarial Patch Generation Method Based on Latent Diffusion Model—0
Natural Image Denoising with Convolutional Networks—0
Blockwise SURE Shrinkage for Non-Local Means—0
Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis—0
Block-matching in FPGA—0
Neighborhood filters and the decreasing rearrangement—0
Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume—0
Neural Affine Grayscale Image Denoising—0
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All datasetsSIDDDNDELD SonyA7S2 x200SID SonyA7S2 x250ELD SonyA7S2 x100SID x100SID x300SID SonyA7S2 x100urban100 sigma15Urban100 sigma50BSD68 sigma50SID SonyA7S2 x300

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CGNetPSNR (sRGB)40.39—Unverified
2KBNetPSNR (sRGB)40.35—Unverified
3NAFNetPSNR (sRGB)40.3—Unverified
4SSAMANPSNR (sRGB)40.08—Unverified
5PNGANPSNR (sRGB)40.07—Unverified
6RestormerPSNR (sRGB)40.02—Unverified
7HINetPSNR (sRGB)39.99—Unverified
8MAXIM-3SPSNR (sRGB)39.96—Unverified
9Uformer-BPSNR (sRGB)39.89—Unverified
10SRMNetPSNR (sRGB)39.72—Unverified
#ModelMetricClaimedVerifiedStatus
1DualDnPSNR (sRGB)40.59—Unverified
2PNGANPSNR (sRGB)40.18—Unverified
3SSAMANPSNR (sRGB)40.05—Unverified
4RestormerPSNR (sRGB)40.03—Unverified
5Uformer-BPSNR (sRGB)39.98—Unverified
6NBNetPSNR (sRGB)39.89—Unverified
7MIRNetPSNR (sRGB)39.88—Unverified
8MAXIM-3SPSNR (sRGB)39.84—Unverified
9MPRNetPSNR (sRGB)39.8—Unverified
10SADNetPSNR (sRGB)39.59—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)44.95—Unverified
2PMNPSNR (Raw)44.51—Unverified
3SFRNPSNR (Raw)44.1—Unverified
4LLDPSNR (Raw)43.84—Unverified
5ELDPSNR (Raw)43.43—Unverified
6LRDPSNR (Raw)43.32—Unverified
7Paired Data(SID)PSNR (Raw)41.97—Unverified
8StarlightPSNR (Raw)40.86—Unverified
9ExposureDiffusion (UNet+ELD)PSNR (Raw)40.39—Unverified
10Noise FlowPSNR (Raw)39.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)41.02—Unverified
2PMNPSNR (Raw)40.92—Unverified
3SFRNPSNR (Raw)40.22—Unverified
4LLDPSNR (Raw)39.76—Unverified
5Paired Data (SID)PSNR (Raw)39.6—Unverified
6ELDPSNR (Raw)39.44—Unverified
7LEDPSNR (Raw)39.34—Unverified
8LRDPSNR (Raw)39.25—Unverified
9StarlightPSNR (Raw)36.25—Unverified
10Noise FlowPSNR (Raw)35.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)46.74—Unverified
2PMNPSNR (Raw)46.5—Unverified
3SFRNPSNR (Raw)46.02—Unverified
4LLDPSNR (Raw)45.61—Unverified
5ELDPSNR (Raw)45.45—Unverified
6LRDPSNR (Raw)44.95—Unverified
7Paired Data(SID)PSNR (Raw)44.47—Unverified
8StarlightPSNR (Raw)43.8—Unverified
9Noise FlowPSNR (Raw)41.05—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)43.36—Unverified
2PMNPSNR (Raw)43.16—Unverified
3SFRNPSNR (Raw)42.29—Unverified
4LLDPSNR (Raw)42.1—Unverified
5SID (paired real data)PSNR (Raw)42.06—Unverified
6ELDPSNR (Raw)41.95—Unverified
7StarlightPSNR (Raw)40.47—Unverified
8Noise FlowPSNR (Raw)38.89—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)37.8—Unverified
2PMNPSNR (Raw)37.77—Unverified
3SFRNPSNR (Raw)36.87—Unverified
4Paired Data(SID)PSNR (Raw)36.85—Unverified
5LLDPSNR (Raw)36.76—Unverified
6ELDPSNR (Raw)36.36—Unverified
7StarlightPSNR (Raw)32.99—Unverified
8Noise FlowPSNR (Raw)32.29—Unverified
#ModelMetricClaimedVerifiedStatus
1PMNPSNR (Raw)43.16—Unverified
2SFRNPSNR (Raw)42.29—Unverified
3LEDPSNR (Raw)41.98—Unverified
4ELDPSNR (Raw)41.95—Unverified
5LRDPSNR (Raw)41.95—Unverified
#ModelMetricClaimedVerifiedStatus
1AKDTAverage PSNR35.64—Unverified
2MaIR+PSNR35.42—Unverified
3MaIRPSNR35.35—Unverified
4SCUNet SCUNetAverage PSNR35.18—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR30.41—Unverified
2MaIRPSNR30.3—Unverified
3SCUNet SCUNetPSNR30.14—Unverified
4AKDTPSNR29.82—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR30.08—Unverified
2MaIRPSNR28.66—Unverified
#ModelMetricClaimedVerifiedStatus
1LEDPSNR (Raw)36.67—Unverified
2LRDPSNR (Raw)36.03—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR33.3—Unverified
2MaIRPSNR33.22—Unverified
#ModelMetricClaimedVerifiedStatus
1R3LPSNR27.67—Unverified
#ModelMetricClaimedVerifiedStatus
1BRGMLPIPS0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1BRGMLPIPS0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1ExposureDiffusion (UNet+paired data)PSNR (Raw)36.82—Unverified
#ModelMetricClaimedVerifiedStatus
1PNGANPSNR40.78—Unverified
#ModelMetricClaimedVerifiedStatus
1PNGANPSNR40.55—Unverified
#ModelMetricClaimedVerifiedStatus
1absDLODRMSE0.07—Unverified