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 3671–3680 of 3874 papers

TitleStatusHype
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception ModuleCode0
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in NetworkCode0
Enhanced Deep Residual Networks for Single Image Super-ResolutionCode1
High-Quality Face Image SR Using Conditional Generative Adversarial NetworksCode0
End-to-End Learning of Video Super-Resolution with Motion Compensation—0
Image Super-Resolution via Deep Recursive Residual NetworkCode0
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Adversarial Image Alignment and Interpolation—0
Super-Resolution via Deep Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified