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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1200 of 1589 papers

TitleStatusHype
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
On-Device Text Image Super Resolution—0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets—0
Dense U-net for super-resolution with shuffle pooling layer—0
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
EPSR: Edge Profile Super resolution—0
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network—0
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution—0
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-ResolutionCode0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
FAN: Frequency Aggregation Network for Real Image Super-resolution—0
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
AdderSR: Towards Energy Efficient Image Super-Resolution—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Interpretable Deep Multimodal Image Super-Resolution—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Image Super-Resolution using Explicit Perceptual Loss—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Cascade Convolutional Neural Network for Image Super-Resolution—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
E-FCNN for tiny facial expression recognition—0
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Binarized Neural Network for Single Image Super Resolution—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniquesCode0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Learning with Privileged Information for Efficient Image Super-Resolution—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
A deep primal-dual proximal network for image restoration—0
Show:102550
← PrevPage 24 of 32Next →
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
8CPAT+PSNR29.36—Unverified
9SwinFIRPSNR29.36—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