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 621–630 of 3874 papers

TitleStatusHype
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time seriesCode1
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Image-specific Convolutional Kernel Modulation for Single Image Super-resolutionCode1
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Cross-Scale Internal Graph Neural Network for Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified