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 1051–1100 of 1589 papers

TitleStatusHype
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution—0
Dense Dual-Attention Network for Light Field Image Super-Resolution—0
Error-correcting neural networks for semi-Lagrangian advection in the level-set method—0
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform—0
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution—0
Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models—0
In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission—0
Scene Text Image Super-Resolution via Parallelly Contextual Attention NetworkCode0
Boosting Lightweight Single Image Super-resolution via Joint-distillationCode0
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment—0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
ParaDiS: Parallelly Distributable Slimmable Neural Networks—0
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
Toward Real-World Super-Resolution via Adaptive Downsampling Models—0
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN—0
Attention-based Multi-Reference Learning for Image Super-ResolutionCode0
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution—0
Multi-Attributed and Structured Text-to-Face Synthesis—0
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
LAConv: Local Adaptive Convolution for Image Fusion—0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank—0
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution—0
Image restoration quality assessment based on regional differential information entropy—0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment—0
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution—0
Blind Image Super-Resolution: A Survey and Beyond—0
Impact of deep learning-based image super-resolution on binary signal detection—0
Blind Image Super-Resolution via Contrastive Representation Learning—0
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network—0
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution—0
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks—0
Fourier Space Losses for Efficient Perceptual Image Super-Resolution—0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising—0
Self-Organized Residual Blocks for 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