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 1921–1930 of 3874 papers

TitleStatusHype
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New BenchmarkCode0
Cross-receptive Focused Inference Network for Lightweight Image Super-ResolutionCode1
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images—0
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
Learning Local Implicit Fourier Representation for Image WarpingCode1
Memory Efficient Patch-based Training for INR-based GANs—0
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution—0
Variational Deep Image RestorationCode1
PS^2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing—0
Continuous Sign Language Recognition via Temporal Super-Resolution Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified