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 1761–1770 of 3874 papers

TitleStatusHype
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method—0
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention—0
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging—0
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection—0
Image Restoration by Deep Projected GSURE—0
Fast Randomized-MUSIC for mm-Wave Massive MIMO Radars—0
Fast Online Video Super-Resolution with Deformable Attention Pyramid—0
Convolutional Bipartite Attractor Networks—0
A Comprehensive Survey of Transformers for Computer Vision—0
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified