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 1301–1310 of 3874 papers

TitleStatusHype
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving—0
Deep learning at scale for subgrid modeling in turbulent flows—0
AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging—0
Back-Projection Pipeline—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified