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 541–550 of 3874 papers

TitleStatusHype
Prior Knowledge Distillation Network for Face Super-Resolution—0
One Model for Two Tasks: Cooperatively Recognizing and Recovering Low-Resolution Scene Text Images by Iterative Mutual Guidance—0
Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity CluesCode0
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical FlowCode1
A Sinkhorn Regularized Adversarial Network for Image Guided DEM Super-resolution using Frequency Selective Hybrid Graph Transformer—0
PlainUSR: Chasing Faster ConvNet for Efficient Super-ResolutionCode1
Super-Resolution via Learned Predictor—0
Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring—0
HSIGene: A Foundation Model For Hyperspectral Image GenerationCode2
Image inpainting for corrupted images by using the semi-super resolution GAN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified