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 3851–3860 of 3874 papers

TitleStatusHype
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening FrameworkCode0
The Little Engine that Could: Regularization by Denoising (RED)Code0
Second-Order Attention Network for Single Image Super-ResolutionCode0
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution NetworkCode0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel RemovalCode0
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural NetworksCode0
Fast and Accurate Single Image Super-Resolution via Information Distillation NetworkCode0
Segmentation and Generation of Magnetic Resonance Images by Deep Neural NetworksCode0
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified