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 1311–1320 of 3874 papers

TitleStatusHype
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results—0
2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts—0
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution—0
Backdoor Attacks against Image-to-Image Networks—0
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images—0
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content—0
GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution—0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified