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 901–950 of 1589 papers

TitleStatusHype
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
Image Super-Resolution With Deep Variational Autoencoders—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Image-to-image domain adaptation for vehicle re-identification—0
Image Transformer—0
IMDeception: Grouped Information Distilling Super-Resolution Network—0
Impact of deep learning-based image super-resolution on binary signal detection—0
Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution—0
Improved Super-Resolution Convolution Neural Network for Large Images—0
Improved Super Resolution of MR Images Using CNNs and Vision Transformers—0
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms—0
Improving Generative Adversarial Networks for Video Super-Resolution—0
Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution—0
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN—0
Infrared Image Super-Resolution via Lightweight Information Split Network—0
Infrared Image Super-Resolution via GAN—0
In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission—0
Interpretable Deep Multimodal Image Super-Resolution—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
Inter-Task Association Critic for Cross-Resolution Person Re-Identification—0
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets—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
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
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
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—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
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—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
Large coordinate kernel attention network for lightweight image super-resolution—0
Large Receptive Field Networks for High-Scale Image Super-Resolution—0
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
LATIS: Lambda Abstraction-based Thermal Image Super-resolution—0
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—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
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