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 961–970 of 3874 papers

TitleStatusHype
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Lightweight Image Super-Resolution with Information Multi-distillation NetworkCode1
Designing a Practical Degradation Model for Deep Blind Image Super-ResolutionCode1
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-ResolutionCode1
Lightweight Image Super-Resolution with Superpixel Token InteractionCode1
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Memory-Augmented Non-Local Attention for Video Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified