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 751–760 of 3874 papers

TitleStatusHype
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
Cylin-Painting: Seamless 360 Panoramic Image Outpainting and BeyondCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Learning from History: Task-agnostic Model Contrastive Learning for Image RestorationCode1
Learning Large-Factor EM Image Super-Resolution with Generative PriorsCode1
Learning Light Field Angular Super-Resolution via a Geometry-Aware NetworkCode1
Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great AgainCode1
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution NetworksCode1
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
Efficient Image Super-Resolution Using Pixel AttentionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified