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 1421–1430 of 3874 papers

TitleStatusHype
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New BenchmarkCode0
Joint Super-Resolution and Alignment of Tiny FacesCode0
End-to-End Optimization of Metasurfaces for Imaging with Compressed SensingCode0
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Joint Reconstruction and Spatial Super-Resolution of Hyper-Spectral CTIS Images via Multi-Scale RefinementCode0
IterInv: Iterative Inversion for Pixel-Level T2I ModelsCode0
Joint High Dynamic Range Imaging and Super-Resolution from a Single ImageCode0
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face RecognitionCode0
ELSR: Extreme Low-Power Super Resolution Network For Mobile DevicesCode0
Adversarial Feedback LoopCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified