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 2391–2400 of 3874 papers

TitleStatusHype
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution—0
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation—0
Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior—0
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
IFF: A Super-resolution Algorithm for Multiple Measurements—0
IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution—0
Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models—0
Image Deconvolution with Deep Image and Kernel Priors—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified