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 1401–1410 of 3874 papers

TitleStatusHype
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution NetworksCode1
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
Integrated Super-Resolution Sensing and Communication with 5G NR Waveform: Signal Processing with Uneven CPs and Experiments—0
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks—0
SR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate ReductionCode0
Detecting disruption of HER2 membrane protein organization in cell membranes with nanoscale precision—0
Neural Architecture Search for Intel Movidius VPU—0
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising—0
Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution—0
AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified