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 3221–3230 of 3874 papers

TitleStatusHype
Deep Blind Hyperspectral Image Fusion—0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal CorrelationsCode0
Super-resolution photoacoustic and ultrasound imaging with sparse arrays—0
Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Frame and Feature-Context Video Super-Resolution—0
Learning to Have an Ear for Face Super-Resolution—0
Multi-grained Attention Networks for Single Image Super-Resolution—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified