Hierarchical Neural Network Improves Style Transfer for Chinese Woodblock Art
A new hierarchical network enhances style transfer for Chinese woodblock art by improving semantic coherence, structural preservation, and color modeling.
A new hierarchical network enhances style transfer for Chinese woodblock art by improving semantic coherence, structural preservation, and color modeling.
A new study proposes a graph neural network tuned by a modified zebra optimization algorithm that models relationships among visual ...
Researchers report a deep learning framework called ShearFocusNet that denoises low-dose CT images by combining Shearlet-based directional perception with spatial ...
Researchers have developed an anisotropic diffusion algorithm that reconstructs missing image regions by steering diffusion along edges, extending from binary ...
Researchers propose SteRGAN, a GAN-based image steganography framework that achieves PSNR values above 41 dB on ImageNet and Microsoft COCO.
A systematic benchmark of fifteen activation functions across CT, mammography and MRI deblurring finds that the spatially adaptive Funnel ReLU ...
Researchers have developed a self-supervised deep learning model, SA-S2S, that removes random noise from 3D seismic data without requiring clean ...
A hybrid particle swarm and evolutionary optimization algorithm uses multilevel thresholding to isolate green maize seedlings from complex field backgrounds ...
A two-stage framework combining high-boost filtering with discrete cosine transformation achieved the strongest reported performance on four grayscale multi-focus image ...
Researchers in Naples have developed EDOKS, a transparent image similarity metric that combines Earth Mover's Distance texture analysis with Oklab ...
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© 2026 Scienmag - Science Magazine