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 1671–1680 of 3874 papers

TitleStatusHype
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation—0
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging—0
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results—0
DiffFNO: Diffusion Fourier Neural Operator—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Block-Based Multi-Scale Image Rescaling—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified