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 1781–1790 of 3874 papers

TitleStatusHype
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter—0
A Simple Plugin for Transforming Images to Arbitrary Scales—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Controlling Neural Networks via Energy Dissipation—0
A Frequency Domain Neural Network for Fast Image Super-resolution—0
Image Super-Resolution Using T-Tetromino Pixels—0
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning—0
Fast Image Super-Resolution Based on In-Place Example Regression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified