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 1711–1720 of 3874 papers

TitleStatusHype
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis—0
Depth Super-Resolution from Explicit and Implicit High-Frequency Features—0
Analog Neural Computing with Super-resolution Memristor Crossbars—0
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs—0
AccelIR: Task-Aware Image Compression for Accelerating Neural Restoration—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
Depth Super Resolution by Rigid Body Self-Similarity in 3D—0
Depth Separable architecture for Sentinel-5P Super-Resolution—0
Blind Hyperspectral-Multispectral Image Fusion via Graph Laplacian Regularization—0
Depth Anything with Any Prior—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified