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
Multi-Attributed and Structured Text-to-Face Synthesis—0
edge-SR: Super-Resolution For The MassesCode1
SwinIR: Image Restoration Using Swin TransformerCode3
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
spectrai: A deep learning framework for spectral dataCode1
Light Field Image Super-Resolution with TransformersCode1
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image RescalingCode1
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-ResolutionCode1
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
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
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment—0
Image restoration quality assessment based on regional differential information entropy—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
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual ReconstructionCode1
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and KernelCode1
Blind Image Super-Resolution via Contrastive Representation Learning—0
Text Prior Guided Scene Text Image Super-resolutionCode1
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
Image Super-Resolution With Non-Local Sparse AttentionCode1
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
Practical Single-Image Super-Resolution Using Look-Up TableCode1
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network—0
One-to-many Approach for Improving Super-ResolutionCode1
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV MinimizationCode1
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
Task Transformer Network for Joint MRI Reconstruction and Super-ResolutionCode1
Variational AutoEncoder for Reference based Image Super-ResolutionCode1
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks—0
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-ResolutionCode1
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity EstimationCode1
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
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed TomographyCode1
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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