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 2811–2820 of 3874 papers

TitleStatusHype
Omniscient Video Super-Resolution—0
In-Place Scene Labelling and Understanding with Implicit Scene Representation—0
Training a Task-Specific Image Reconstruction Loss—0
JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution—0
Multi-frame Super-resolution from Noisy Data—0
Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy—0
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution—0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network—0
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling—0
Gauging diffraction patterns: field of view and bandwidth estimation in lensless holography—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified