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 711–720 of 3874 papers

TitleStatusHype
Advancing High-Resolution Video-Language Representation with Large-Scale Video TranscriptionsCode1
Local Texture Estimator for Implicit Representation FunctionCode1
Image-specific Convolutional Kernel Modulation for Single Image Super-resolutionCode1
Pansharpening by convolutional neural networks in the full resolution frameworkCode1
Physics-Informed Neural Operator for Learning Partial Differential EquationsCode1
TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle ImagingCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
Learning Continuous Representation of Audio for Arbitrary Scale Super ResolutionCode1
Scale-Aware Dynamic Network for Continuous-Scale Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified