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 1731–1740 of 3874 papers

TitleStatusHype
Using Super-Resolution Imaging for Recognition of Low-Resolution Blurred License Plates: A Comparative Study of Real-ESRGAN, A-ESRGAN, and StarSRGAN—0
A Wideband Distributed Massive MIMO Channel Sounder for Communication and Sensing—0
PAON: A New Neuron Model using Padé Approximants—0
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint—0
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models—0
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder—0
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation—0
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction—0
PFStorer: Personalized Face Restoration and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified