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 1361–1370 of 3874 papers

TitleStatusHype
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
Ensemble Super-Resolution with A Reference DatasetCode0
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution ImagingCode0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing ImagesCode0
Component Attention Guided Face Super-Resolution Network: CAGFaceCode0
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT SimulationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified