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 711–720 of 3874 papers

TitleStatusHype
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-ResolutionCode1
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World ScenesCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-ResolutionCode1
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV MinimizationCode1
Enhanced Deep Residual Networks for Single Image Super-ResolutionCode1
Enhanced Quadratic Video InterpolationCode1
Enhanced Semantic Extraction and Guidance for UGC Image Super ResolutionCode1
Cross-receptive Focused Inference Network for Lightweight Image Super-ResolutionCode1
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC AlgorithmCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified