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 1371–1380 of 3874 papers

TitleStatusHype
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution ImagingCode0
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance RegularizationCode0
Component Attention Guided Face Super-Resolution Network: CAGFaceCode0
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT SimulationCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
3DSRnet: Video Super-resolution using 3D Convolutional Neural NetworksCode0
Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing ImagesCode0
COMISR: Compression-Informed Video Super-ResolutionCode0
LAR-SR: A Local Autoregressive Model for Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified