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 1061–1070 of 3874 papers

TitleStatusHype
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models—0
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution—0
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns—0
STAR-Pose: Efficient Low-Resolution Video Human Pose Estimation via Spatial-Temporal Adaptive Super-Resolution—0
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
Compressed Video Super-Resolution based on Hierarchical Encoding—0
ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection—0
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified