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 541–550 of 3874 papers

TitleStatusHype
Deep Space-Time Video Upsampling NetworksCode1
Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great AgainCode1
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
Deep Learning-based Face Super-Resolution: A SurveyCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
Cascaded Temporal Updating Network for Efficient Video Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified