SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

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Showing 1001–1050 of 1589 papers

TitleStatusHype
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Medical image super-resolution method based on dense blended attention network—0
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution—0
Metric Imitation by Manifold Transfer for Efficient Vision Applications—0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Mitigating Channel-wise Noise for Single Image Super Resolution—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors—0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network—0
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution—0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images—0
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution—0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination—0
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution—0
MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution—0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network—0
MRI Super-Resolution with Ensemble Learning and Complementary Priors—0
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better—0
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution—0
MSRA-SR: Image Super-resolution Transformer with Multi-scale Shared Representation Acquisition—0
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution—0
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution—0
Multi-Attributed and Structured Text-to-Face Synthesis—0
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection—0
Multi-Contrast Super-Resolution MRI Through a Progressive Network—0
Multi-frame image super-resolution with fast upscaling technique—0
Multi-grained Attention Networks for Single Image Super-Resolution—0
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution—0
Multi-image Super-resolution via Quality Map Associated Attention Network—0
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution—0
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution—0
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform—0
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing—0
Multimodal Deep Unfolding for Guided Image Super-Resolution—0
Multimodal Image Super-resolution via Deep Unfolding with Side Information—0
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach—0
Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution—0
Multi-scale Image Super Resolution with a Single Auto-Regressive Model—0
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution—0
Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation—0
Hypernetwork functional image representation—0
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution—0
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS—0
Neural Differential Equations for Single Image Super-resolution—0
NLCUnet: Single-Image Super-Resolution Network with Hairline Details—0
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy—0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54—Unverified
2HMA†PSNR29.51—Unverified
3Hi-IR-LPSNR29.49—Unverified
4HAT-LPSNR29.47—Unverified
5HAT_FIRPSNR29.44—Unverified
6DRCTPSNR29.4—Unverified
7HATPSNR29.38—Unverified
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—Unverified
10CPATPSNR29.34—Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16—Unverified
2HMA†PSNR28.13—Unverified
3Hi-IR-LPSNR28.13—Unverified
4HAT-LPSNR28.09—Unverified
5HAT_FIRPSNR28.07—Unverified
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—Unverified
8HATPSNR28.05—Unverified
9CPATPSNR28.04—Unverified
10SwinFIRPSNR28.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72—Unverified
2DRCT-LPSNR28.7—Unverified
3HMA†PSNR28.69—Unverified
4HAT-LPSNR28.6—Unverified
5HAT_FIRPSNR28.43—Unverified
6DRCTPSNR28.4—Unverified
7HATPSNR28.37—Unverified
8CPAT+PSNR28.33—Unverified
9CPATPSNR28.22—Unverified
10PFTPSNR28.2—Unverified