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 2171–2180 of 3874 papers

TitleStatusHype
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution—0
Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data—0
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment—0
Local Patch Encoding-Based Method for Single Image Super-Resolution—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
LocalSR: Image Super-Resolution in Local Region—0
Local Statistics for Generative Image Detection—0
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution—0
LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow—0
Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified