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 3711–3720 of 3874 papers

TitleStatusHype
Cross-Resolution Learning for Face RecognitionCode0
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image SynthesisCode0
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-ResolutionCode0
Remote Sensing Image Fusion Based on Two-stream Fusion NetworkCode0
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale WarpingCode0
Webcam-based Pupil Diameter Prediction Benefits from UpscalingCode0
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution PipelineCode0
Cross-domain heterogeneous residual network for single image super-resolutionCode0
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch NormalizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified