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 3551–3560 of 3874 papers

TitleStatusHype
End-to-end pipeline for simultaneous temperature estimation and super resolution of low-cost uncooled infrared camera frames for precision agriculture applications—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
What's in the Image? Explorable Decoding of Compressed Images—0
Adversarial Deep-Unfolding Network for MA-XRF Super-Resolution on Old Master Paintings Using Minimal Training Data—0
Exploring Deep Learning Image Super-Resolution for Iris Recognition—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation—0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
End-to-End Learning of Video Super-Resolution with Motion Compensation—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified