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 1091–1100 of 3874 papers

TitleStatusHype
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution—0
Generalized super-resolution 4D Flow MRI x2013 using ensemble learning to extend across the cardiovascular systemCode0
LATIS: Lambda Abstraction-based Thermal Image Super-resolution—0
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution—0
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics—0
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations—0
Scene Text Image Super-resolution based on Text-conditional Diffusion ModelsCode1
Emu Edit: Precise Image Editing via Recognition and Generation Tasks—0
DSR-Diff: Depth Map Super-Resolution with Diffusion ModelCode0
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without NoiseCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified