SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 411–420 of 3874 papers

TitleStatusHype
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image GenerationCode1
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
EDVR: Video Restoration with Enhanced Deformable Convolutional NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified