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 2341–2350 of 3874 papers

TitleStatusHype
High-Resolution Be Aware! Improving the Self-Supervised Real-World Super-Resolution—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
High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces—0
High-Similarity-Pass Attention for Single Image Super-Resolution—0
High-throughput lensless whole slide imaging via continuous height-varying modulation of tilted sensor—0
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution—0
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution—0
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified