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 701–710 of 3874 papers

TitleStatusHype
Light Field Image Super-Resolution Using Deformable ConvolutionCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Efficient scene text image super-resolution with semantic guidanceCode1
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale LearningCode1
Creating High Resolution Images with a Latent Adversarial GeneratorCode1
Efficient Video Compression via Content-Adaptive Super-ResolutionCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified