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 1341–1350 of 3874 papers

TitleStatusHype
Content and Colour Distillation for Learning Image Translations with the Spatial Profile LossCode0
Learning Descriptor Networks for 3D Shape Synthesis and AnalysisCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
SR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate ReductionCode0
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
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing ImagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified