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 2411–2420 of 3874 papers

TitleStatusHype
Image Restoration by Deep Projected GSURE—0
Progressive Image Super-Resolution via Neural Differential Equation—0
Image Restoration using Autoencoding Priors—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
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
Image Super-Resolution using Explicit Perceptual Loss—0
Image Superresolution using Scale-Recurrent Dense Network—0
Image Super-Resolution Using T-Tetromino Pixels—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified