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 2001–2010 of 3874 papers

TitleStatusHype
DL4DS -- Deep Learning for empirical DownScalingCode1
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-ResolutionCode1
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution—0
VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution—0
Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis—0
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization—0
TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network—0
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites—0
Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified