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 451–460 of 3874 papers

TitleStatusHype
Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodlesCode1
Revisiting Implicit Neural Representations in Low-Level VisionCode1
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and ResultsCode1
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B ImageryCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
Cross-View Hierarchy Network for Stereo Image Super-ResolutionCode1
Gated Multi-Resolution Transfer Network for Burst Restoration and EnhancementCode1
Local-Global Temporal Difference Learning for Satellite Video Super-ResolutionCode1
Towards Realistic Ultrasound Fetal Brain Imaging SynthesisCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified