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 801–850 of 1589 papers

TitleStatusHype
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets—0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution—0
Iris super-resolution using CNNs: is photo-realism important to iris recognition?—0
Exploring Deep Learning Image Super-Resolution for Iris Recognition—0
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Every Pixel Tells a Story: End-to-End Urdu Newspaper OCR—0
Is Image Super-resolution Helpful for Other Vision Tasks?—0
ISTA-Inspired Network for Image Super-Resolution—0
Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution—0
EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset—0
Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models—0
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution—0
J-Net: Improved U-Net for Terahertz Image Super-Resolution—0
Joint Dictionary Learning for Example-based Image Super-resolution—0
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensing—0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model—0
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution—0
Kalman-Inspired Feature Propagation for Video Face Super-Resolution—0
Kernel Adversarial Learning for Real-world Image Super-resolution—0
Kernel based low-rank sparse model for single image super-resolution—0
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution—0
Error-correcting neural networks for semi-Lagrangian advection in the level-set method—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
EPSR: Edge Profile Super resolution—0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
L^2FMamba: Lightweight Light Field Image Super-Resolution with State Space Model—0
LAConv: Local Adaptive Convolution for Image Fusion—0
Language Independent Single Document Image Super-Resolution using CNN for improved recognition—0
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization—0
Enhancing Traffic Scene Predictions with Generative Adversarial Networks—0
Large coordinate kernel attention network for lightweight image super-resolution—0
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior—0
Large Receptive Field Networks for High-Scale Image Super-Resolution—0
Towards Realistic Data Generation for Real-World Super-Resolution—0
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
LATIS: Lambda Abstraction-based Thermal Image Super-resolution—0
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey—0
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
Advanced Super-Resolution using Lossless Pooling Convolutional Networks—0
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution—0
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding—0
Learned Multi-View Texture Super-Resolution—0
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer—0
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image 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
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