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 2821–2830 of 3874 papers

TitleStatusHype
Test-time Cost-and-Quality Controllable Arbitrary-Scale Super-Resolution with Variable Fourier Components—0
Test-time Training for Hyperspectral Image Super-resolution—0
Text-guided Explorable Image Super-resolution—0
TextIR: A Simple Framework for Text-based Editable Image Restoration—0
S^3Mamba: Arbitrary-Scale Super-Resolution via Scaleable State Space Model—0
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance—0
Texture-Based Error Analysis for Image Super-Resolution—0
Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution—0
Texture Hallucination for Large-Factor Painting Super-Resolution—0
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified