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 1931–1940 of 3874 papers

TitleStatusHype
Joint Multiple FMCW Chirp Sequence Processing for Velocity Estimation and Ambiguity Resolving—0
Joint Multitarget Detection and Tracking with mmWave Radar—0
Joint OAM Radar-Communication Systems: Target Recognition and Beam Optimization—0
Joint Range-Velocity-Azimuth Estimation for OFDM-Based Integrated Sensing and Communication—0
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks—0
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation—0
Joint Spatial and Angular Super-Resolution from a Single Image—0
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network—0
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution—0
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified