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

Showing 526550 of 1589 papers

TitleStatusHype
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial NetworkCode1
ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolutionCode1
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual LearningCode1
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature LearningCode1
Hyperspectral Image Super Resolution with Real Unaligned RGB GuidanceCode1
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
Real-time 6K Image Rescaling with Rate-distortion OptimizationCode1
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark DatasetCode1
Exploiting Raw Images for Real-Scene Super-ResolutionCode1
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
A Tree-guided CNN for image super-resolutionCode1
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods0
Enhance the Image: Super Resolution using Artificial Intelligence in MRI0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network0
Content-adaptive Representation Learning for Fast Image Super-resolution0
A Study in Dataset Pruning for Image Super-Resolution0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP0
ConsisSR: Delving Deep into Consistency in Diffusion-based Image Super-Resolution0
Enhanced generative adversarial network for 3D brain MRI super-resolution0
Con-Patch: When a Patch Meets its Context0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks0
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks0
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified