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 2671–2680 of 3874 papers

TitleStatusHype
Frequency Consistent Adaptation for Real World Super Resolution—0
Attention-based Image Upsampling—0
Zoom-to-Inpaint: Image Inpainting with High-Frequency DetailsCode1
Deep Learning Techniques for Super-Resolution in Video Games—0
Neural Radiance Flow for 4D View Synthesis and Video ProcessingCode1
CT Super Resolution via Zero Shot Learning—0
Projected Distribution Loss for Image EnhancementCode0
Polyblur: Removing mild blur by polynomial reblurring—0
TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images—0
Learning Continuous Image Representation with Local Implicit Image FunctionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified