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 1951–1960 of 3874 papers

TitleStatusHype
Kernel Adversarial Learning for Real-world Image Super-resolution—0
Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing—0
Kernel Aware Resampler—0
Kernel based low-rank sparse model for single image super-resolution—0
KernelFusion: Assumption-Free Blind Super-Resolution via Patch Diffusion—0
Kernelized Back-Projection Networks for Blind Super Resolution—0
When to Use Convolutional Neural Networks for Inverse Problems—0
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified