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 411–420 of 3874 papers

TitleStatusHype
Diffusion Models Beat GANs on Image ClassificationCode1
Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-ResolutionCode1
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-ResolutionCode1
Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational ImagingCode1
Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRICode1
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion ModelsCode1
WaveMixSR: A Resource-efficient Neural Network for Image Super-resolutionCode1
RBSR: Efficient and Flexible Recurrent Network for Burst Super-ResolutionCode1
SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology ImageCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified