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 14261450 of 1589 papers

TitleStatusHype
Data Upcycling Knowledge Distillation for Image Super-ResolutionCode0
Progressive Image Deraining Networks: A Better and Simpler BaselineCode0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid NetworkCode0
Progressive Perception-Oriented Network for Single Image Super-ResolutionCode0
Kernel Modeling Super-Resolution on Real Low-Resolution ImagesCode0
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected NetworkCode0
Edge-Informed Single Image Super-ResolutionCode0
Spatially-Variant Degradation Model for Dataset-free Super-resolutionCode0
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniquesCode0
Trustworthy Image Super-Resolution via Generative PseudoinverseCode0
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse CodingCode0
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
DWA: Differential Wavelet Amplifier for Image Super-ResolutionCode0
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SRCode0
Adaptive Densely Connected Super-Resolution ReconstructionCode0
Dual-Stream Fusion Network for Spatiotemporal Video Super-ResolutionCode0
RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall DownscalingCode0
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic SimulationsCode0
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
MAMNet: Multi-path Adaptive Modulation Network for Image Super-ResolutionCode0
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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