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 1961–1970 of 3874 papers

TitleStatusHype
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—0
DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation—0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Differentiable Search for Finding Optimal Quantization Strategy—0
DiffFNO: Diffusion Fourier Neural Operator—0
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation—0
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics—0
DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits—0
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified