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 1311–1320 of 3874 papers

TitleStatusHype
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution—0
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion ModelsCode1
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT SimulationCode0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
WaveMixSR: A Resource-efficient Neural Network for Image Super-resolutionCode1
A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank ApproximationCode0
RBSR: Efficient and Flexible Recurrent Network for Burst Super-ResolutionCode1
Cutting-Edge Techniques for Depth Map Super-Resolution—0
SPDER: Semiperiodic Damping-Enabled Object Representation—0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified