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 2541–2550 of 3874 papers

TitleStatusHype
On the modern deep learning approaches for precipitation downscaling—0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution—0
On the Use of Singular Value Decomposition as a Clutter Filter for Ultrasound Flow Imaging—0
On training deep networks for satellite image super-resolution—0
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution—0
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution—0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images—0
Uncertainty-Driven Loss for Single Image Super-Resolution—0
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified