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 1071–1080 of 3874 papers

TitleStatusHype
Multi-scale Residual Network for Image Super-ResolutionCode0
Multi-scale super-resolution generation of low-resolution scanned pathological imagesCode0
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object RepresentationCode0
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-NetworkCode0
Multi-scale deep neural networks for real image super-resolutionCode0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified