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 701–750 of 1589 papers

TitleStatusHype
Time-lapse image classification using a diffractive neural network—0
Towards Robust Drone Vision in the Wild—0
HST: Hierarchical Swin Transformer for Compressed Image Super-resolutionCode1
Learning Degradation Representations for Image DeblurringCode1
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolutionCode1
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-ResolutionCode1
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGANCode1
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
Robust Real-World Image Super-Resolution against Adversarial AttacksCode1
GLEAN: Generative Latent Bank for Image Super-Resolution and BeyondCode5
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging—0
Reference-based Image Super-Resolution with Deformable Attention TransformerCode1
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
Improved Super Resolution of MR Images Using CNNs and Vision Transformers—0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Semantic uncertainty intervals for disentangled latent spacesCode0
Image Super-Resolution with Deep DictionaryCode1
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
Towards Lightweight Super-Resolution with Dual Regression LearningCode2
Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical FidelityCode1
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
Rethinking Super-Resolution as Text-Guided Details Generation—0
Perception-Oriented Stereo Image Super-Resolution—0
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
Cross-receptive Focused Inference Network for Lightweight Image Super-ResolutionCode1
Variational Deep Image RestorationCode1
Exploring the solution space of linear inverse problems with GAN latent geometry—0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics—0
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution—0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Real-World Single Image Super-Resolution Under Rainy Condition—0
Hypernetwork-Based Adaptive Image RestorationCode1
Real-World Light Field Image Super-Resolution via Degradation ModulationCode1
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps—0
Robust Deep Ensemble Method for Real-world Image DenoisingCode0
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution—0
Patch-based image Super Resolution using generalized Gaussian mixture model—0
Real-World Image Super-Resolution by Exclusionary Dual-LearningCode1
Real-Time Super-Resolution for Real-World Images on Mobile Devices—0
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net—0
Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices—0
ShuffleMixer: An Efficient ConvNet for Image Super-ResolutionCode1
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
Image Super-resolution with An Enhanced Group Convolutional Neural NetworkCode1
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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