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 2011–2020 of 3874 papers

TitleStatusHype
BSRA: Block-based Super Resolution Accelerator with Hardware Efficient Pixel Attention—0
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
CogView2: Faster and Better Text-to-Image Generation via Hierarchical TransformersCode2
Generative Adversarial Networks for Image Super-Resolution: A Survey—0
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive TransformerCode1
Attentive Fine-Grained Structured Sparsity for Image RestorationCode1
Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency—0
Unsupervised Blur Kernel Estimation and Correction for Blind Super-ResolutionCode0
IMDeception: Grouped Information Distilling Super-Resolution Network—0
Learn from Unpaired Data for Image Restoration: A Variational Bayes ApproachCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified