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 2851–2860 of 3874 papers

TitleStatusHype
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Regularization by Denoising via Fixed-Point Projection (RED-PRO)—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Learning to Learn to Compress—0
Sparse Based Super Resolution Multilayer Ultrasonic Array Imaging—0
Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions—0
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision—0
Video compression with low complexity CNN-based spatial resolution adaptation—0
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified