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 3631–3640 of 3874 papers

TitleStatusHype
InverseNet: Solving Inverse Problems with Splitting Networks—0
FSRNet: End-to-End Learning Face Super-Resolution with Facial PriorsCode0
BLADE: Filter Learning for General Purpose Computational Photography—0
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning—0
xUnit: Learning a Spatial Activation Function for Efficient Image RestorationCode0
The Perception-Distortion TradeoffCode0
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution—0
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution—0
Remote Sensing Image Fusion Based on Two-stream Fusion NetworkCode0
Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified