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 11261150 of 1589 papers

TitleStatusHype
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features0
A deep primal-dual proximal network for image restoration0
Cross-Scale Internal Graph Neural Network for Image Super-ResolutionCode1
HypervolGAN: An efficient approach for GAN with multi-objective training function0
SRFlow: Learning the Super-Resolution Space with Normalizing FlowCode1
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping0
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection NetworksCode1
Real-World Super-Resolution via Kernel Estimation and Noise InjectionCode2
Progressively Unfreezing Perceptual GAN0
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual LearningCode1
Exploring Sparsity in Image Super-Resolution for Efficient InferenceCode1
iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networksCode1
Neural Sparse Representation for Image RestorationCode1
Learning Texture Transformer Network for Image Super-ResolutionCode1
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars MiningCode1
Robust Reference-Based Super-Resolution With Similarity-Aware Deformable ConvolutionCode1
Residual Feature Aggregation Network for Image Super-Resolution0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis0
Dual Super-Resolution Learning for Semantic SegmentationCode1
Inter-Task Association Critic for Cross-Resolution Person Re-Identification0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks0
Perceptual Extreme Super Resolution Network with Receptive Field BlockCode1
Single Image Super-Resolution via Residual Neuron Attention Networks0
Iterative Network for Image Super-ResolutionCode1
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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