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 1181–1190 of 3874 papers

TitleStatusHype
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Deep Learning-Based Channel EstimationCode0
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimationCode0
Adaptive Densely Connected Super-Resolution ReconstructionCode0
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
MemNet: A Persistent Memory Network for Image RestorationCode0
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
Masked Autoencoders are PDE LearnersCode0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
Deep Laplacian Pyramid Networks for Fast and Accurate Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified