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 3441–3450 of 3874 papers

TitleStatusHype
The Effects of Super-Resolution on Object Detection Performance in Satellite ImageryCode0
Supervised Deep Kriging for Single-Image Super-Resolution—0
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss—0
Binary Document Image Super Resolution for Improved Readability and OCR PerformanceCode0
Why Are Deep Representations Good Perceptual Quality Features?—0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal LearningCode0
MAMNet: Multi-path Adaptive Modulation Network for Image Super-ResolutionCode0
Image Reconstruction with Predictive Filter FlowCode0
Patch-based Progressive 3D Point Set UpsamplingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified