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 1521–1530 of 3874 papers

TitleStatusHype
Image Super-Resolution via Dual-State Recurrent NetworksCode0
Image Super-resolution via Feature-augmented Random ForestCode0
Image Super-Resolution via Attention based Back Projection NetworksCode0
Image Super-Resolution via Deep Recursive Residual NetworkCode0
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical RectificationCode0
Edge-Informed Single Image Super-ResolutionCode0
Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-ResolutionCode0
Image Super-Resolution Using Dense Skip ConnectionsCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified