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 751–800 of 1589 papers

TitleStatusHype
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning—0
E-FCNN for tiny facial expression recognition—0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction—0
Effective Invertible Arbitrary Image Rescaling—0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment—0
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution—0
Efficient Image Super-Resolution via Symmetric Visual Attention Network—0
Efficient Learnable Collaborative Attention for Single Image Super-Resolution—0
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution—0
Efficient Multi-disparity Transformer for Light Field Image Super-resolution—0
Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices—0
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation—0
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection—0
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution—0
Efficient Super Resolution For Large-Scale Images Using Attentional GAN—0
Efficient Super Resolution Using Binarized Neural Network—0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool—0
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
Enhanced generative adversarial network for 3D brain MRI super-resolution—0
Enhance the Image: Super Resolution using Artificial Intelligence in MRI—0
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods—0
Enhancing Frequency for Single Image Super-Resolution with Learnable Separable Kernels—0
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding—0
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution—0
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior—0
Enhancing Traffic Scene Predictions with Generative Adversarial Networks—0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
EPSR: Edge Profile Super resolution—0
Error-correcting neural networks for semi-Lagrangian advection in the level-set method—0
ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensing—0
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models—0
EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset—0
Every Pixel Tells a Story: End-to-End Urdu Newspaper OCR—0
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Exploring Deep Learning Image Super-Resolution for Iris Recognition—0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Exploring the solution space of linear inverse problems with GAN latent geometry—0
Facial Attribute Capsules for Noise Face 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