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 3061–3070 of 3874 papers

TitleStatusHype
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Attention Based Real Image RestorationCode0
Radar Accurate Localization of UAV Swarms Based on Range Super-Resolution Method—0
Mining self-similarity: Label super-resolution with epitomic representationsCode0
RAIN: A Simple Approach for Robust and Accurate Image Classification NetworksCode0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization—0
Single Pair Cross-Modality Super Resolution—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified