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 3011–3020 of 3874 papers

TitleStatusHype
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges—0
When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study—0
When to Use Convolutional Neural Networks for Inverse Problems—0
Wider Channel Attention Network for Remote Sensing Image Super-resolution—0
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data—0
W-Net: A Facial Feature-Guided Face Super-Resolution Network—0
XAI-based gait analysis of patients walking with Knee-Ankle-Foot orthosis using video cameras—0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
XCycles Backprojection Acoustic Super-Resolution—0
Dynamic Attention-Guided Diffusion for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified