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 2491–2500 of 3874 papers

TitleStatusHype
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution—0
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization—0
Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis—0
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation—0
Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization—0
Self-Enhanced Convolutional Network for Facial Video Hallucination—0
Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Self-Organized Residual Blocks for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified