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 3251–3260 of 3874 papers

TitleStatusHype
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation—0
Robust Online Video Super-Resolution Using an Efficient Alternating Projections Scheme—0
Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval—0
Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging—0
Robust Regression via Deep Negative Correlation Learning—0
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors—0
Progressive Face Super-Resolution via Attention to Facial LandmarkCode0
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors—0
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified