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 681–690 of 3874 papers

TitleStatusHype
Revisiting RCAN: Improved Training for Image Super-ResolutionCode1
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model InversionCode1
Self-Supervised Deep Blind Video Super-ResolutionCode1
Flexible Style Image Super-Resolution using Conditional ObjectiveCode1
Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-ResolutionCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-ResolutionCode1
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
Learnable Lookup Table for Neural Network QuantizationCode1
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified