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 3011–3020 of 3874 papers

TitleStatusHype
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention—0
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal LearningCode1
Towards a Computer Vision Particle Flow—0
Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks—0
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial LearningCode1
Closed-loop Matters: Dual Regression Networks for Single Image Super-ResolutionCode1
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Learning Enriched Features for Real Image Restoration and EnhancementCode1
Instant recovery of shape from spectrum via latent space connectionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified