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 3311–3320 of 3874 papers

TitleStatusHype
SUNLayer: Stable denoising with generative networks—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System—0
SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation—0
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution—0
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model—0
Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs—0
AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified