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 1651–1660 of 3874 papers

TitleStatusHype
Image-to-image domain adaptation for vehicle re-identification—0
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss—0
High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging—0
High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage—0
Infrared Image Super-Resolution via Lightweight Information Split Network—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
Deep Image Super Resolution via Natural Image Priors—0
High-throughput lensless whole slide imaging via continuous height-varying modulation of tilted sensor—0
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution—0
Federated Learning for Blind Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified