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 1411–1420 of 3874 papers

TitleStatusHype
Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE LayerCode0
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New BenchmarkCode0
Joint Reconstruction and Spatial Super-Resolution of Hyper-Spectral CTIS Images via Multi-Scale RefinementCode0
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Joint Super-Resolution and Alignment of Tiny FacesCode0
End-to-End Optimization of Metasurfaces for Imaging with Compressed SensingCode0
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR VideoCode0
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution ImagingCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified